Selection of wireless technology for tracking construction materials using a fuzzy decision model/Belaidzio rysio technologiju atranka statybinems medziagoms stebeti, taikant neapibreztuju aibiu sprendimo modeli.
Jiang, Shaohua ; Jang, Won-Suk ; Skibniewski, Miroslaw J. 等
1. Introduction
Due to the information-intensive nature of construction projects,
it is crucial that engineers, inspectors, and maintenance personnel have
on-demand access to construction project data (Behzadan et al. 2008) so
they can make real-time decisions (Khoury, Kamat 2009). However, there
is a severe lack of up-to-date as-built information about construction
projects, and the current practice of manually collecting monitoring
data is error-prone, expensive, inaccurate, and inefficient (Grau et al.
2009; Navon, Sacks 2007; Sacks et al. 2005). Advanced wireless tracking
technology for construction assets offers multiple benefits, and can be
used for optimizing productivity and cost-saving, as well as the obvious
safety and security applications with improved efficiencies and
effectiveness, thus providing competitive advantages.
In recent years, a wide range of advanced wireless tracking
technology solutions have been developed and applied unprecedented to
realize a ubiquitous computing environment in many industries. Numerous
research studies have developed approaches for applying wireless
tracking technologies to construction projects and
facility/infrastructure management, and in particular, construction site
assets tracking. Examples of the types of technologies addressed in
these studies include radio frequency identification (RFID), global
positioning systems (GPS), combination of RFID and GPS, Wi-Fi,
Bluetooth, Zigbee, and Ultra Wideband (UWB) (Jaselskis, El-Misalami
2003; Domdouzis et al. 2007; Ergen et al. 2007; Woo et al. 2011; Lu et
al. 2007; Jang, Skibniewski 2009; Teizer et al. 2008; Giretti et al.
2009).
The construction environment is characterized as a spatially
expansive, object-cluttered, fast-changing, and harsh environment,
including both indoor and outdoor environments. Because of the unique
nature of construction sites, large amounts of dispersed materials,
tools, equipment, and vehicles must be well positioned to provide
construction resources in the right place at the right time, which is
quite different from other industries. On the other hand, there are a
variety of indoor and outdoor location tracking technologies with
significantly different characteristics, infrastructure, and device
requirements (Behzadan et al. 2008). In addition, different kinds of
wireless tracking technologies have different functionalities,
capabilities, and scopes of application (Fig. 1). Furthermore, it is
well documented that a single technology may provide different
functionalities and capabilities in different application areas
depending on the application's requirements. For example, RFID used
in local construction crew monitoring may require exact positioning data
and real-time data updates, whereas a nation-wide construction
procurement system using RFID may need a higher level of readability and
an expandable network infrastructure.
[FIGURE 1 OMITTED]
Because of the heterogeneous and unique characteristics of the
construction industry, it is difficult for decision makers to select the
right technology for the right application without economic and
functional loss. Until now, little research has been conducted on the
selection of appropriate wireless tracking technologies for use at
construction sites. In addition, the selection of wireless technologies
for the construction environment from among the increasing number of
technology-alternatives requires challenging multi-criteria
decision-making by infrastructure project stakeholders. Consequently, it
is essential to encourage construction engineers to select the most
suitable wireless tracking technology solution based on their
application requirements to make full use of the technologies. This
research aims to develop a decision-making model for selecting wireless
technologies for construction assets tracking, and to suggest a
multi-criteria fuzzy approach for making an appropriate decision from
among the various alternatives.
2. An overview of the wireless technologies available for
construction assets tracking
In this study, several wireless tracking technology solutions
applied to construction assets tracking were considered, including RFID,
GPS, a combination of RFID and GPS, Wi-Fi, Bluetooth, Zigbee, and UWB.
Below are details about the application of each of these technologies to
the construction sector.
2.1. Radio frequency identification (RFID) technology
RFID is a type of automatic identification technology in which
radio frequencies are used to capture and transmit data from a tag or
transponder and store data in a distributed fashion. A typical RFID
system is comprised of two main components: A reader and a tag. A tag,
which consists of an electronic chip coupled with an antenna, is
attached to an object and stores data about the object. The reader
reads/writes data from/to a tag via radio frequencies and transfers data
to a host computer. Reading/writing ranges depend on the operation
frequency (low, high, ultra high, and microwave), and whether the tag
requires a battery to operate (active versus passive). Active tags
typically have higher reading ranges; however, they have limited life
spans, requiring periodic battery replacement (Kiziltas et al. 2008).
RFID technology has specific features that make it suitable for the
construction field. RFID can dynamically transmit and receive
information to identify objects without "line-of-sight" and
does not require close proximity, individual readings, or direct
contact. RFID technology enables data entry and access at any time
throughout the lifecycle of the tag. Information stored on the tag can
be modified, which provides flexibility for managerial medication (Ko
2009). RFID tags can even be read from long distances and are durable in
the harsh environment of a construction site (Jaselskis, El-Misalami
2003; Song et al. 2005; Ergen et al. 2007).
A wide range of applications of RFID in the construction industry
have been explored, such as tool inventory and allocation, receiving and
keeping track of a variety of pipe spool components used in
process-piping construction, precast production management, and so on
(Goodrum et al. 2006; Yin et al. 2009).
2.2. Global positioning system (GPS) technology
GPS is a satellite-based radio-navigation system used for tracking
objects in outdoor environments. GPS is based on measuring the time
required for radio signals to travel from a specific number of
satellites, whose positions are known at each moment, to a receiver. GPS
receivers calculate the distance and determine locations in terms of
longitude, latitude, and altitude, with great accuracy (Oloufa et al.
2003). After 20 years of development, the current stand-alone GPS
systems can lock-in positions with an accuracy of around 10 m.
Furthermore, a positioning accuracy of 1-2 m can be achieved with
differential GPS (DGPS) technology, which uses a GPS base receiver
located at a known fixed point and applies differential corrections to
the observations from a rover receiver. Real-time kinematic GPS (RTK
GPS) can further enhance positioning accuracy to the centimeter (even
millimeter) level by combining the measurements of the signal carrier
phases from both the base and rover receivers with special algorithms
(Lu et al. 2007).
GPS technologies have received particular attention from
construction researchers in that they can provide cost-effective
solutions to automated data collection. Research has indicated that GPS
is a significant step forward in revolutionizing current practices in
tracking, managing, and controlling assets, such as equipment, vehicles,
and pedestrians (Peyret et al. 2000; Oloufa et al. 2003; Saeed et al.
2010).
2.3. A combination of RFID and GPS technologies
To benefit from the merits of both RFID and GPS, a solution
combining RFID and GPS was presented. Using combined active UHF RFID and
GPS technologies, Song et al. (2005) provided a logical mechanism for
locating materials that are scattered at a construction site based on a
proximity method. In this approach, construction sites are scanned in
detail daily to identify the location of materials at a given site. This
approach provides approximate locations for materials at a construction
site, and can be used as a front-end solution for identifying
components' initial locations in a storage yard. In the storage
yard of a manufacturing plant, Ergen et al. (2007) proposed an automated
system using RFID technology combined with GPS technology that uniquely
identified pre-cast components and then tracked and located them using
little to no worker input.
2.4. Wi-Fi
Currently, the most prominent specification for IEEE 802.11 WLAN
standards is the Wi-Fi alliance. Wi-Fi operates in the license-free 2.4
GHz industrial, scientific, and medical (ISM) band. Received signal
strength indicator (RSSI) is widely adopted, and the accuracy of typical
Wi-Fi positioning systems is approximately 3-30 m, with an update rate
in the range of a few seconds (Vossiek et al. 2003). It provides wired
LAN extension or replacement in a range of market areas (e.g.,
enterprises, homes, and hot spots) (Shen et al. 2008).
WLAN has distinct advantages. First, it is an economical solution
because WLAN systems usually already exist as part of the communications
infrastructure. In WLAN mobile devices, the positioning system can be
implemented simply in the software. Second, the WLAN-based positioning
system covers a large area and may function across many buildings.
Third, it is a stable system because of its robust radio frequency
signal propagation (Xiang et al. 2004). Some research efforts have
applied WLAN, including Wi-Fi, in construction assets tracking, such as
the identification of construction entities visible in a user's
field of view (Khoury, Kamat 2007), as well as labor tracking (Woo et
al. 2011).
2.5. Bluetooth
Originally designed as a short-range wireless connectivity solution
for personal, portable, and hand-held electronic devices, Bluetooth
technology radios operate on a license-free, globally available
2.4000-2.4835 GHz industrial, scientific, and medical (ISM) band, which
is divided into 79 channels. In addition, Bluetooth employs a fast,
frequency-hopping spread spectrum (FHSS) technology (with an incremental
frequency of 1600 Hz) to avoid interference in the ISM band and ensure
the reliability of data communications (Chatschik 2001; Lu et al. 2007).
Bluetooth radio can be classified into three power classes based on
RF transmission power. The typical working distance for Bluetooth ranges
from 10 to 100 m, depending on the power class of the device. At
present, Class 3 Bluetooth with a 10 m radius is embedded in most
commercial Bluetooth applications (Hallberg et al. 2003). A Bluetooth
device assumes the role of either a master or a slave. The master
regulates what slave will transmit data and when. In some cases, two
types of devices share a common hardware structure and thus can swap
their master-slave roles only by altering the core programs. Bluetooth
is an industry specification for ensuring compatibility in wireless
connectivity of electronic devices, allowing one manufacture's
master device to control the slave device made by another. The longer
communication range of Bluetooth (an optional 100 m standard is
available off-the-shelf compared with less than 20 m for most RFID
solutions) may substantially broadened the application domain of
Bluetooth (Lu et al. 2007).
With respect to the utilization of Bluetooth in construction
engineering, Lu et al. (2007) embedded Bluetooth technology in roadside
beacons for positioning construction vehicles at building sites. In
their field trials, they found that the communication range of the
Bluetooth module was reduced from a nominal 20 m to 100 m because of the
complex conditions at the site.
2.6. Zigbee
As an emerging wireless communication technology, ZigBee has the
capability of realizing a ubiquitous environment. ZigBee is a product of
the ZigBee Alliance, an organization of manufacturers dedicated to
developing a new networking technology, and is aimed at industrial and
home wireless applications (Jang, Skibniewski 2009). ZigBee
specification takes advantage of the IEEE 802.15.4 wireless protocols as
the communications method, and expands on this with a flexible mesh
network, wide range of applications, and interoperability. A ZigBee
network consists of ZigBee coordinators, ZigBee routers, and ZigBee end
devices. The end devices conduct multi-hop communications via connected
routers to communicate with other devices connected to the networks.
Using the advantages associated with flexible ad hoc networking, the
promise of the ZigBee application can be found in the robust, reliable,
self-configuring, and self-healing networks that provide a simple,
cost-effective, and battery-efficient approach to adding wireless to
mobile and fixed communication devices. ZigBee supports many industrial
applications, including construction automation, structural health
monitoring, and automated control and operations, all of which can
benefit from the advantages of the technology.
As for the application of ZigBee in the construction industry,
Skibniewski and Jang (2009) proposed a ZigBee-based wireless sensor
network for object tracking and monitoring in construction processes. By
using ZigBee wireless sensors, Lee et al. (2009) presented a Webbased
system to monitor the greenhouse gas emissions released by construction
equipment. Jang and Skibniewski (2009) introduced system architecture
for automated materials tracking in construction processes by deploying
these Zigbee networks.
2.7. UWB
Ultra wide band (UWB) is a wireless technology for the low-power
transferring of large amounts of digital data through a wide spectrum of
frequencies over short distances. Some major distinctive advantages of
UWB technology include high immunity to interference from other radio
systems, high multipath immunity, high data rate, and fine range
resolution capability (Shen et al. 2008). The tags in an UWB tracking
system decide the localization dimensionality, and reception by three or
more receivers permits accurate 2D localizations, whereas reception by
four or more receivers allows for precise 3D localization. If only one
or two receivers can receive a tag transmission, proximity detection can
also be readily accomplished (Khoury, Kamat 2009). Because of its short
pulse radio frequency (RF), waveforms and large bandwidth, UWB provides
fine time resolution and has good potential for applications in ranging
and positioning, and good immunity to multipath effects in indoor
applications.
In recent years, UWB technology has been successfully applied in
the construction industry. Some examples from both research and industry
are as follows. Teizer et al. (2008) presented automated real-time
three-dimensional location sensing for a construction resource
(workforce, equipment, and materials) positioning and tracking system
using UWB technology. Giretti et al. (2009) reported the design and
development of a proactive advanced system that can perform real-time
position tracking using UWB and can predict risky events. Chehri et al.
(2009) proposed that an UWB-based wireless sensor network (WSN) be
adopted as a solution for locating equipment and miners in underground
mines.
3. Decision criteria for selecting wireless technology
A number of wireless technologies have evolved that support various
industrial applications, including building and construction automation,
structural health monitoring, and automated control and operation. The
main motivation for the deployment of these technologies is to enhance
communication efficiency over wired systems, while at the same time
reducing the cost and effort associated with its use. With multiple
functionalities and services, wireless technologies provide a potential
opportunity; however, there are still many concerns that have prevented
mass adoption of the technology among the diverse alternatives for the
wireless tracking systems at construction sites. From a user's
viewpoint, decision makers may ask the following major questions when
considering the possible selection of these technologies: 1) What is the
sensing or communication ranges required to transmit reliable data,
ensure maximum quality of service, and measure accuracy? 2) What density
of nodes is needed to configure the networks for optimally efficient
cost and power management? 3) What measurement interval is required to
collect the most meaningful data? 4) How useful is the wireless
technology for general users, requiring minimum effort for programming,
installation, control, and management?
As the technology has evolved, selecting an optimal solution from
among the various types of wireless technologies has become more
difficult because the similar specifications of wireless technologies
provides different functionalities, capacities, and costs. For instance,
radio frequency identification (RFID) would be superior to UWB in terms
of cost savings and ease of use, but RFID may be the wrong solution if
an application requires high performance in network flexibility or
tracking accuracy. Consequently, understanding the detailed technical
functionality that each technology provides for a specific application
is critical. At the same time, justification for why a specific decision
criterion should be considered in a given application environment and
type of technology should be provided.
In this research, we investigated the practical issues in the
possible deployment of these technologies and summarized the decision
criteria that should be used by a decision maker when he or she chooses
a construction tracking system from among the multiple alternatives.
Cost
Because the construction industry has been faced with adopting
technology innovations for various projects, cost planners or decision
makers must consider the appropriate costs for different phases of a
construction
process that are required to efficiently complete the project.
Typically, the preparation of cost planning for adopting new
technologies is vital early in the construction process because
successful implementation of the technologies are often manifested in
return on investment. When cost is taken into account for adopting a
wireless sensor network, there are a number of issues to consider: 1)
the number of sensor nodes; 2) monetary value, such as device or
installation costs; and 3) maintenance costs once the technology is
deployed.
First, the number of nodes required for deployment is the most
significant factor in any large-scale application domain such as a
construction site. A higher density of sensor nodes reduces the overall
uncertainty by increasing the accuracy and quality with which events are
sensed. On the other hand, cost trade-offs are possible when a high
density of sensor nodes are deployed. Consequently, a preliminary
investigation of the optimum density levels corresponding to a
reasonable deployment cost should be examined. Second, there is no
general rule of thumb to evaluate deployment costs in terms of dollars
and cents. Deployment strategies depend on various factors such as
actual needs, the purpose of the application, the construction
environment, the types of sensors, routing scheme, and so on. Because
deployment strategies are heterogeneous, it is not easy for sensor
developers to estimate the quantitative benefits of using a sensor
network in construction applications. At the same time, this uncertainty
makes it difficult for users to adopt new and promising technologies in
their applications. Third, because of the limited lifespan of WSNs, as
the number of nodes increases, the maintenance costs also have the
potential to hamper the adoption of wireless sensor networks. It is not
practical to deploy hundreds or thousands of nodes when their batteries
must be changed every month or even every year. The cost to investigate
and replace failed components in a large network could also be a
practical challenge. By integrating the issues described above into the
proposed decision-making model, these cost criteria were divided into
several sub-criteria, such as device costs, installation costs, and
maintenance costs.
Performance
Major progress in the practical deployment of WSN solutions has
been made in the past few years; however, it is still a challenge to
convince construction engineers to use WSNs in their diverse
applications because various types of wireless standards meet the
different needs and requirements of various materials tracking systems.
Consequently, the most critical criterion for successfully adopting
these technologies for materials tracking systems is reviewing the level
of performance and functionality of the WSNs. Generally, this would
entail both practical and technical issues for many practitioners. In
terms of practical measurements, wireless networks should be more
reliable than wired systems because they provide a more accurate,
real-time, and robust framework in the construction environment
(Skibniewski, Jang 2009). However, location accuracy on a nanometer
scale or zero-delay in RF transmission may be overcapacity in
implementing a material tracking system. For most decision makers, more
often than not, optimal levels of performance and functionality for a
typical tracking system are the most desirable criteria that they are
willing to consider in this situation. Second, these practical
measurements are also associated with many technical issues in which the
general expectations for WSN performance meets the needs of construction
engineers in terms of: 1) packet delivery rate; 2) bit error rate; 3)
duty-cycle and latency; 4) fault tolerance; 5) time synchronization; 6)
throughput and so on.
Although various performance sub-criteria could affect the outcomes
of a decision-making analysis, a preliminary survey of construction
engineers indicated that accuracy and data rate were primarily
considered sub-criteria to performance criteria. More than 70% of
responders answered that accuracy was the major criterion, such that a
certain level of accuracy should be provided even though low-level
accuracy was generally required in a construction material tracking
system. The remaining 30% of responders indicated that data rate was
also important when it came to the type of data used. For example, some
wireless protocols are not efficient or cannot carry a video stream.
Thus, the data rate becomes important to making a decision when the
material tracking system is designed to leverage high-tech media with a
required amount of data transmission.
Flexibility
Wireless technology should be also scalable and flexible to
dynamically expand and adapt to the changes that occur at physical
construction sites. Autonomous configuration with guaranteed coverage
and scalability would increase the probability of detecting
geographically constrained phenomena or events in construction
environments. At the same time, a variety of application strategies
could be implemented because of the guaranteed reliability and
networking capacities of wireless technologies. Because of the nature of
the distribution of WSN frameworks, the design for detailed task
assignments and corrections made by sensor networks are becoming more
complex. Hence, a higher level of abstraction of the low-level hardware
layer should be provided so that the applications can be easily
implemented.
As new technologies emerge, the different levels of network
flexibility and interoperability will make it difficult for decision
makers to select wireless technologies. Most personal wireless area
networks (PWAN) do not feature a big enough coverage range to cover an
entire construction site with only one-hop communication. For instance,
Zigbee supported by the IEEE802.15.4 protocol has an indoor coverage
range of 10-30 meters and an outdoor coverage range of 50-100 meters
(MaxStream 2007); RFID and Bluetooth have reliable communication over
even shorter distances (a few meters). Thus, if networking flexibility
is not guaranteed, relatively short communication distances may be a
technical barrier to fully deploying PWAN. By assuring wireless
connectivity in a highly dynamic and complex environment, a framework of
WSNs could provide the networking configuration and multihop capability
that could efficiently expand the network throughout this large-scale
application domain.
Interoperability is another key to successfully integrating various
wireless technologies into construction tracking applications. WSNs in
most construction applications require heterogeneous collaboration among
multiple participants in various sectors, and different hardware and
software platforms in a building need to be interoperated with other
types of building platforms. WSN compatibility with existing hardware
and software will be key to many construction applications. For example,
if WSNs are deployed with BACnet-based building automation systems,
wireless sensors for resource tracking systems must be well interfaced
with the existing architecture of the BACnet protocol (McGowan 2005). In
this existing platform, the data-centric design of WSNs should provide
sufficient knowledge-sharing with the BACnet application layer, and the
networking protocol should be compatible enough to leverage the full
capabilities of the installed network. Thus, sensor networks can be
easily adapted to the parts of the BACnet modules tailored to the
application requirements.
Considering the decision criteria discussed above, in this paper,
we categorized networking flexibility and interoperability as
sub-criterion under flexibility. For the networking flexibility
sub-criterion, the following decision criteria were identified: 1)
coverage range, which might affect the number of sensor nodes, network
density, and quality of wireless connectivity; 2) communication
efficiency, which might affect transmission reliability and networking
performance; and 3) topology, which might affect the layout of sensor
nodes and the networking configuration.
Interference
Wireless construction tracking applications must function over
heterogeneous networks with multidimensional types of sensors and
networks. At this point, careful planning and consideration of the way
that the wireless communication is achieved in the typical construction
environment is required. As a wireless signal travels back and forth
through the air, signal interference caused by multipath or obstructions
becomes one of the key issues in evaluating WSN performance. In open
spaces, the received power is inversely proportional to the square of
the distance between the receiver and the transmitter; thus, it is
obvious that the received power between the receiver and the transmitter
must be estimated. However, it is more complicated when walls, floors,
equipment, or temporarily stocked materials are present because when the
RF signal bounces off these objects, it causes complicated signal
attenuation or distortion. Signal attenuation or distortion is often
referred to as obstruction, and this interference affects the
reliability of wireless communication. For example, very low received
power caused by obstruction may increase the frequency of packet loss,
resulting in an overall decrease of packet delivery rate in the system.
This type of interference is not preventable if wireless nodes will be
placed in a layout such as that at a construction site where various
objects are already placed or installed. Thus, signal reliability should
be quantitatively evaluated carefully, so that network topology can be
accordingly configured to provide high signal strength, link quality,
and packet delivery rate in a situation with obstructions.
Another factor that can affect interference is the coexistence
problem when multiple sensor nodes access a single access point
simultaneously or all the channels are in use (Shin et al. 2007). This
becomes critical if wireless devices are operated in a high-density area
or if multiple wireless devices are operated at the same bandwidth are
used in a construction site (e.g., co-existence of Zigbee, Wi-Fi,
Bluetooth, and microwave ovens operated at 2.4 GHz). In this case,
technical problems such as transmission delays or packet collisions can
occur, resulting in unreliable wireless communication. There are some
technical solutions such as non-overlapping channel selection, radio
resource management, or dynamic frequency selection. However, a more
important remedy for construction engineers is designing the network
topologies so that the co-existence effect can be minimized by placing
the different types of devices off their interference coverage.
Maintenance
The major advantage of wireless and battery-powered technology over
a wired system is said to be the decreased installation and maintenance
costs. The absence of cable reduces the human intervention required to
inspect cable connectivity and manage the complicated wiring through the
entire lifecycle of the devices. This reduction in labor and maintenance
for wireless technology frameworks directly increases labor productivity
such that autonomous configuration of the sensor system automatically
gathers and transfers field information at a construction site.
Consequently, in the long-term, lifecycle maintenance for tracking
construction assets would benefit from the minimal use of labor and
increased labor productivity.
However, there are some technical challenges in maintaining
wireless technologies. First, wireless sensor units used for tracking
applications at a construction site must stand up to harsh outdoor
environments: high humidity during the rainy season, heat during the day
or summer seasons, strong winds or external impact, electromagnetic
fields from other test instruments, and attachment conditions when the
sensors are placed on construction materials. These environmental
factors can make providing a reliable system very difficult, and thus,
is an important decision factor when deployed.
Second, energy sources for wireless technologies are very limited
and they usually depend on batteries. Normally, the radio component in
wireless sensor accounts for the largest energy consumption. Radios are
operated through four distinct modes, e.g. transmit, receive, idle; and
transmit and receive mode are the largest portions in energy
consumption. Typical consumption rate of Zigbee in transmit and receive
mode, for example, are 18.8 and 17.4 mA, respectively (Texas Instrument
2007), and the sleep mode may provide the significant energy savings
when the wireless devices are inactive. With these radio's modes
and low-power strategy, it is possible that battery-powered wireless
sensor systems could theoretically last for years according to the
duty-cycle. Even though the low power sensor technologies are rapidly
being developed by many sensor companies to improve the power
management, this energy limitation becomes still critical when hundreds
or thousands of nodes are placed in a network for long-term tracking
applications. Thus, it may be impractical to frequently change or
recharge the batteries in such a large number of nodes. For this reason,
power management issues always arise in practical wireless sensor
applications, and there are often deals maintenance costs in long-term
applications. Issues of power management are often associated with the
technical design of the routing scheme, the MAC & PHY layers,
throughput, and topology. However, application-specific requirements
also play an important role in addressing practical strategies for power
management. Such requirements might be associated with the following
questions: What time interval is sufficient for monitoring construction
materials in a stockyard? How large a coverage area will provide a
reliable tracking system? Should event detection or data collection be
used? Should passive or active sensing be used? Should data logging or
real-time monitoring be used?
Practicability
In general, programming and integrating a commercial platform are
relatively difficult in WSNs. Unlike PC-based platform, programming
activity for WSN must run on the sensor hardware, which has
significantly limited resources in memory and capacity. For example,
ATmega128L in Micaz provides 8 MIPS throughput with 4 Kbytes data
memory, 128 Kbytes program memory, and 512 EEPROM (Crossbow Technology
Inc. 2007). Given limited hardware resources, spatial and temporal
complexity should be well defined to utilize the capacity profile of a
sensor node fully. An important consideration is that programming
architecture must follow an energy efficiency design philosophy. Because
of battery operation, special care must be taken to optimally arrange
tasks and commands for computations and communications, which are the
major factors in power consumption. Another issue is that programming
must be supported in the existing OS architecture. Currently available
OS platforms that provide reliable dynamic memory allocation, sufficient
packet size, and multithread concept are very limited. Consequently,
network performance, memory management, and execution model have limited
performance in WSNs, resulting in additional challenges involving
scalability in a large-scale sensor network. This is crucial in
construction applications in which multiple obstructions result in a
scaled down communication range; thus, ad hoc mesh networking can only
provide unique solutions for large deployments.
There must be new programming paradigms and new operating systems
that efficiently satisfy the needs and requirements, supporting
user-friendly architecture, and ensuring maximum reliability and
modularity. Individual sensor nodes must have high adaptability such
that they can be configured in the existing environment and
infrastructure, and an easy framework for installation, modification,
and removal should be provided to convince users of their practical
applications. General construction engineers should easily realize their
expected goals and application purposes with given WSN interfaces in
which minimal programming expertise and efforts are needed. To provide
fully utilized WSN applications, sensor designers and application
developers should keep in mind that "ease of use" is a primary
factor in design philosophy for the eventual success of a WSN. At the
same time, applicability should also be provided to general users: 1)
minimal post-processing of the data collected are needed; 2) typical
device size should be small enough to be useful and durable in the
construction environment without disturbing regular work processes; 3)
available commercially so it is easily adoptable to advancements in
wireless technologies; and 4) firm and optimum attachment should be
guaranteed to increase reliability and applicability of the tracking
system.
4. Research method
This chapter introduces a multi-criteria fuzzy approach to
facilitate decision making when selecting wireless technology. The
Multi-criteria Decision Making (MCDM) model is one of the methods in
decision studies in which the factors necessary for a priority decision
are many (multi-criteria) (Sasmal, Ramanjaneyulu 2008). This approach is
one of the fastest growing areas in decision-making research, and
assists decision makers in converting imprecise and vague criteria into
numerical values (Sreeda, Sattanathan 2009; Nayagam et al. 2011). The
selection of WSN technology, which is a typical multi-criteria decision
problem in which relevant alternatives are selected, evaluated, or
ranked according to a number of criteria, influences a construction
project's tracking effect. Subjectivity, uncertainty, and vagueness
in selecting WSN technology can be dealt with using linguistic
variables. Because linguistic variables can be converted into fuzzy
numbers, fuzzy sets theory has proved very convenient for searching for
solutions to problems that involve subjective opinion (Plebankiewicz
2009) and can be particularly powerful in handling the inherent
uncertainty in MCDM problems (Hajkowicz, Collins 2007; Alipour et al.
2010; Chang, Wang 2009).
4.1. Fuzzy set theory
If we denote a universal set of X, then a fuzzy subset A of X is
defined by its membership function [f.sub.A] (Zadeh 1965):
A = {(x, [f.sub.A]|x < X)},
where membership space M = [0,1]. (1)
The fuzzy membership function assigns each element x in A to a real
number in the interval [0,1]. The fuzzy set generalizes a classical set
and the membership function generalizes the characteristic function.
Bellman and Zadeh (1970) introduced the following concept of fuzzy
decision making using O as the fuzzy objective function (alternatives),
C as the fuzzy constraints, and D as the fuzzy decision:
D = O [intersection] C. (2)
For k fuzzy objectives and m fuzzy constraints, the optimal
decision can be written as a membership function as follows:
D = [O.sub.1] [intersection] [O.sub.2] [intersection] ...
[intersection] [O.sub.k] [intersection] [C.sub.1] [intersection]
[C.sub.2] [intersection] ... [intersection] [C.sub.m]; (3)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
In fuzzy multi-criteria decision-making problems, control and
handling of the membership function are performed by the problem domain
where the fuzziness lies. If the fuzziness in the problems lies in the
objective function coefficients, the membership function can be
expressed by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
and
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)
where [U.sub.k] and [L.sub.k] are the worst upper bound and the
best lower bound of the objective function k, respectively. In the
membership function, maximal grade represented as f = 1 implies the most
probable value for membership function in a given alternative.
In the extension principle suggested by Zadeh (1965), the fuzzy
arithmetic operations of addition, subtraction, multiplication, and
division of two fuzzy numbers, M = ([m.sub.1], [m.sub.2], [m.sub.3]) and
N = ([n.sub.1], [n.sub.2], [n.sub.3]), are as follows:
M [direct sum] N = ([m.sub.1] + [n.sub.1], [m.sub.2] + [n.sub.2],
[m.sub.3] + [n.sub.3]); (8)
M [THETA] N = ([m.sub.1] - [n.sub.1], [m.sub.2] - [n.sub.2],
[m.sub.3] - [n.sub.3]); (9)
M [cross product] N = ([m.sub.1][n.sub.1], [m.sub.2][n.sub.2],
[m.sub.3][n.sub.3]); (10)
M [??] N = ([m.sub.1]/[n.sub.1], [m.sub.2]/[n.sub.2],
[m.sub.3]/[n.sub.3]). (11)
4.2. Linguistic variables
A linguistic variable is defined as a variable whose values are
described qualitatively. This concept is very useful for real world
problems where many of the decision criteria are either complex or not
precisely known. In these situations, the appropriate alternatives for
mathematical modeling in a vague and fuzzy environment are very
difficult to judge. The concept of linguistic values introduced by Zadeh
(1965) aims at the conversion of fuzzy situations to conventional
quantitative expressions that provide a suitable way to evaluate
alternatives and criteria.
Linguistic fuzzy variables are often denoted on a fuzzy scale that
expresses the relative importance of relative weights. For example, a
linguistic scale of "very small (VS)", "too small
(TS)", "smaller than equal (SE)", "equally important
(EI)", "exactly equal (EE)", "larger than equal
(LE)", "too large (TL)", and "very large (VL)"
indicates the relative importance of various criteria or sub criteria. A
graphical representation of a triangular membership function and fuzzy
linguistic scale of importance is shown in Fig. 2 and Table 1.
[FIGURE 2 OMITTED]
4.3. Fuzzy weight of criteria
Buckley (1985) offered a method to measure the relative weights
scale using geometric row mean. Buckley's approach is often
advantageous because its solution is unique and can be applied to both
triangular and trapezoidal fuzzy numbers.
If a reciprocal matrix, A = [[a.sub.ij]], for various criteria, as
well as sub-criteria, is given, the geometric mean for each row is
determined by:
[r.sub.i] = [([m.summation over (j=1)] [a.sub.ij]).sup.1/m] for all
i, (12)
where m is the number of decision criteria.
Then, fuzzy weight, [w.sub.i], is given as:
[w.sub.i] = [r.sub.i] [empty set] ([r.sub.1][direct sum] ...
[direct sum][r.sub.m]). (13)
This fuzzy appropriate index (FAT) was introduced by Chan et al.
(2000) to account for the uncertainties in justifying alternatives and
criteria by aggregating the hierarchy over all the criteria. If
[S.sub.tm] is the weight of an alternative, At, under criterion
[C.sub.m], then the [FAI.sub.t] for each alternative is given as
follows:
[FAI.sub.t] = (1/k) [cross product] [([S.sub.t1] [cross product]
[W.sub.1]) [direct sum] ([S.sub.t2] [cross product] [W.sub.2]) [direct
sum] ... [direct sum]([S.sub.tm] [cross product] [W.sub.m])]. (14)
4.4. Ranking triangular fuzzy numbers
In many practical applications, a ranking method that can give the
possible distribution of alternatives is essential for decision makers.
As we discussed, the fuzzy appropriate index provides a method to
measure the aggregated fuzzy sets over all the alternatives. Thus, the
integrated FAT obtained from all the alternatives and their rankings can
be used as the best alternative.
However, it is sometimes difficult to interpret a fuzzy situation
for a well-accepted choice because comparing fuzzy quantities is
subjective. Accordingly, comparison and choice of the best alternatives
might reflect the decision maker's point of view and reflect
whether her/his personal preference is optimistic or pessimistic.
Kim and Park (1990) proposed a ranking method for comparing fuzzy
numbers considering the possible deviation between the left and right
sides of the membership functions. This method makes the calculations
simple, but it also does not lose information about the decision
maker's bias.
To represent a decision maker with an optimistic point of view, let
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] be the maximizing
set of x and the grade of membership of point x in [G.sub.max] can be
given as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (15)
For a pessimistic point of view, [G.sub.min] can be similarly
defined as the minimizing set and the grade of membership of point x in
[G.sub.min] can be given as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (16)
Also, if [bar.D] = {x \ [f.sub.D](x)} is defined as the fuzzy
decision set, then the grade membership of the decision set can be
expressed by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (17)
where k is an index such that k = 1 represents an optimist and k =
0 represents a pessimist, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN
ASCII] is the membership of the optimist's decision set,
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the membership of
the pessimist's decision set, and n is a fuzzy constraint.
5. Scenario-based decision making application: an example
A decision-making problem for selecting wireless technology is
designed to implement the multi-criteria fuzzy method described in the
previous section. Although wireless technologies have qualitative
benefits over conventional methods when deployed in remote construction
assets tracking, a comprehensive approach for measuring the expected
values has not been fully provided to many construction engineers. This
may be critical for decision makers in planning, operating, and
controlling the construction process when diverse technologies are
available.
This chapter describes a case study of scenario-based decision
making process for real world application. This approach deals with
variability at a construction site level when a construction field
manager tries to select the wireless technologies available in the
market. In order to conduct this case study, we first described the
hypothetical construction project that wireless technologies would be
adopted for assets tracking practices in the construction sites. Then we
developed a parameter modeling for decision making scenario taking into
consideration the decision criteria and functional properties of the
system. Also, the decision making procedure mentioned in the previous
chapter was illustrated. Finally, the results obtained from the
multi-criteria decision making approach was discussed.
While this case study elaborates a decision making process based on
a hypothetical scenario with assumed project parameters, the approach
with a multi-criteria fuzzy method could provide an illustration from
which decision makers in a construction site can benefit for their
practical application.
5.1. Application scenario of WSN selection
Construction sites are generally characterized as a large-scale
application domain with complicated site layout and heterogeneous
obstructions. Examples are the physical size of construction site that
is even larger than several hundreds of thousands square meters, and the
construction environment has a variety of irregular obstructions with
different shapes, geographical locations, material properties and
dielectric characteristics. Such complicated nature of construction
environment often challenge the adoption of wireless technologies in
various applications because wireless signal experiences signal
attenuation, distortion and multipath through/from various types of
obstructions, such as construction walls, equipment, temporary
structures, and constructed facilities. These obstructions are randomly
placed in all around a construction site, affecting the reliability of
wireless signal. In a decision making process, therefore, the site
manager needs to put careful considerations in designing the fuzzy model
based on the realistic decision parameters and technology capabilities.
Examples include quantitative analysis and performance investigation on
the recent wireless technologies for the efficient and successful
adoption.
In order to provide a real-world application scenario, a
hypothetical case scenario was adopted to facilitate understanding about
the application of the fuzzy decision model presented in this paper for
selection of wireless technology for tracking construction materials.
The case scenario is a site relative to a 6 story residential building
built with a reinforced concrete frame structure and pre-cast slabs in
open area close to several other residential buildings. The residential
building's total construction area is 4354.56 [m.sup.2] and the
floor area is 725.76 [m.sup.2] (57.6 m by 12.6 m). The size of site is
an approximate rectangle of 88 m by 33 m including lay down yard and
field office. The major types of construction materials need to be
tracked are precast concrete, cement bag, steel member, etc. Traditional
construction materials tracking mainly rely on costly manual operation
and paper lists, which is labor-intensive, error-prone, and
time-consuming. Wireless technology for construction materials tracking
provides a key to increase productivity, reduce tedious manual
operation, avoid delays, and increase profitability.
5.2. Parameter modeling for decision making
Parameter modeling for the decision making process was developed by
considering the major characteristics of wireless technology and a
typical facility construction project. We first categorized three
essential areas: 1) monetary value; 2) functionality of the technology;
and 3) operational effort. Monetary values such as device cost,
installation cost, and maintenance cost are prior criteria in most
construction project because the successful completion of the project is
often evaluated by the return on investment. Functionality of the
technology is also an important factor, and careful examination about
the technology details can enhance the overall functional benefits and
application purposes. Signal propagation, attenuation, and obstruction
are the unique properties of wireless technology that could affect the
formulation of network, measurement accuracy, interoperability, and
communication efficiency. Thus, second category of functional factor is
divided into three criteria of performance, flexibility and
interference. Third category deals with operational efforts needed to
use and manage the adopted technology. Qualitative improvements by
adopting the new technology are included in this category, such as
maintenance and practicability. Life-cycle maintenance strategy and
practical usage of the technology are also important factors that could
directly affect the field work level to managers and crews when the
technology is adopted and placed in the construction site. Summarizing
the decision parameters above, the Fig. 3 illustrates the proposed
decision making model for selecting wireless technology.
To provide a way to evaluate technology selections, expert
interviews of professional construction engineers were conducted to
verify the proposed fuzzy-based decision-making model. Detailed
descriptions of each criterion are summarized in Chapter 3. The decision
model is comprised of six major criteria ([C.sub.1] to [C.sub.6]) with
the objective of selecting a wireless device for a material tracking
system. In addition, sub-criteria for each major criterion were
considered. The relative importance of each major criterion and
sub-criterion is described in a linguistic scale with eight
alternatives: "very small (VS)", "too small (TS)",
"smaller than equal (SE)", "equally important (EI)",
"exactly equal (EE)", "larger than equal (LE)",
"too large (TL)", and "very large (VL)".
[FIGURE 3 OMITTED]
To justify the objectives for the decision-making problems,
professional construction engineers were asked to make a recommendation
for each criterion as described in Table 2. At the same time, five
different wireless technologies were used in this case study to obtain
the relative importance of alternatives versus criteria:
RFID device ([A.sub.1]): The advantage of RFID is that the
information stored in the tag can be scanned and read without physical
contact with the RFID reader. Unlike a barcode, the tag can be
programmed and reused to contain the useful data, providing mobility and
convenience to many applications, such as asset tracking, supply chain
management, manufacturing control, and fleet management.
GPS device ([A.sub.2]): Unlike other local/personal area networks,
GPS has the unique feature of global accessibility to GPS receivers on a
continuous worldwide basis, thus providing accurate positioning
capability to an unlimited number of people at anytime. GPS mapping, car
navigation, and industrial asset tracking and positioning are the main
areas of application.
Wi-Fi device ([A.sub.3]): Wi-Fi is designed to allow mobile
computers, smart phones, or consumer electronics to have access to other
devices on the network. The relatively high data rate, interoperability,
and Internet protocol security of Wi-Fi are the major advantages to this
certified product that has gained acceptance for use in personal home
networks, businesses, and industries over the conventional wired LAN.
ZigBee device ([A.sub.4]): ZigBee specification is for embedded
applications, such as home automation, mobile services, wireless
sensing, and ubiquitous solutions. The IEEE802.15.4 protocol is aimed at
the inexpensive, self-organizing, expandable mesh networks with the key
feature of communication redundancy that could compensate for the risk
of a single point failure in wired systems.
UWB device ([A.sub.5]): Unlike a specification using narrow band
technology such as 802.11 WLAN or ZigBee, the IEEE802.15.4a UWB in the
range 3.1 to 10.6 GHz provides improved WPAN functionalities, such as
low energy levels, dynamic channel capacity, a 1 Mbps data rate, and
robustness to interference from applications such as home automation,
localization, and other wireless solutions.
5.3. Multi-criteria fuzzy analysis and results
Based on the theoretical explanation in Chapter 4, the procedure of
the multi-criteria fuzzy analysis is explained below:
Step 1. Identify available alternatives and criteria based on the
parameter study for the decision making process. Main criteria are then
classified into several sub-criteria to formulate the hierarchical
decision model.
Step 2. Define linguistic fuzzy scale to provide quantitative
expression for evaluating the alternatives and criteria. Vague and
subjective criteria are then converted into relative importance and
relative weights.
Step 3. Form a reciprocal matrix A from the judgment of
professional experts. The linguistic component of the reciprocal matrix
A is then converted into fuzzy numbers.
Step 4. Calculate and normalize the geometric row means and fuzzy
weight, and apply them to all the criteria and sub-criteria.
Step 5. Aggregate the relative importance over all the criteria and
calculate the fuzzy approximate index for available alternatives.
Step 6. Rank the alternatives by adopting fuzzy approximate index
considering the decision maker's attitude in their opinions.
According to the decision criteria and the linguistic scale, the
expert's opinions in a linguistic description were firstly
converted into a reciprocal matrix that was formulated by fuzzy numbers.
Then, the geometric row mean as described in Eq. (12) was applied to
measure the fuzzy weights of the major criteria and sub-criteria. The
fuzzy weights of all the criteria at each level and the fuzzy weight
evaluation of each technology alternative are shown in Tables 3 and 4,
respectively.
Using fuzzy weights, the fuzzy approximate index (FAI) was adopted
and calculated using Eq. (14) to measure the aggregated fuzzy sets over
all the alternatives. The accumulation of fuzzy weights in FAI provides
insight into how much the membership function of triangular numbers are
biased to the left or right hand sides and where the highest function
value is located. Adopting Kim and Park's approach, each
alternative can be ranked in order of FAI; the largest FAI with the
maximum membership function value is ranked highest (Fig. 4). The
ranking index values for technologies [A.sub.1] to [A.sub.6] are
summarized in Table 5.
[FIGURE 4 OMITTED]
The results of the rankings show that alternative A3 for optimists
and neutral persons is the best choice for a long-distance material
tracking solution, whereas alternative A5 is the best selection for
pessimists. Although there are a variety of factors that might affect
the rankings depending on a person's subjective point of view, the
accumulated fuzzy weight and fuzzy appropriate index obtained from the
subjective opinion of the experts provided insight into the best
selection among the various alternatives. Consequently, the
multi-criteria decision-making approach applied in this research might
assist decision maker's to select the best alternative from among
the many available technologies for which the expected benefits and
advantages are vague or undetermined. It should be noted that the five
alternatives in this research were the selection of the available
technologies that could be perceived as practical devices having general
functionality and performance for application to construction material
tracking. Thus, the results might be different for other industries or
applications.
6. Conclusions
The current practice of manually collecting monitoring data is
error-prone, expensive, inaccurate, and inefficient. The recent advent
of wireless technologies offers an advanced method of data collection
with multiple benefits for optimizing productivity, cost savings,
safety, and security applications with improved efficiency and
effectiveness. While the multiple functionalities and services that
wireless technologies provide have potential application to construction
applications, it is difficult to select one unique decision for
selecting wireless technologies. With this motivation, this research
proposes a multi-criteria fuzzy decision-making model for selecting the
wireless technology for tracking construction assets to obtain a
suitable decision from among the various alternatives.
In the decision-making model, six major criteria were selected and
each criterion was then divided into several sub-criteria to represent
detailed decision factors. Using a multi-criteria fuzzy method,
qualitative opinions were converted to fuzzy numbers to generate fuzzy
weights and a fuzzy approximate index (FAI). Based on the aggregated
FAI, five alternative technologies were ranked based on three decision
maker's perspectives. The rankings showed that Wi-Fi (alternative
A3) was the best choice for a wireless tracking solution for optimists
and neutral persons, whereas UWB (alternative A5) was the best selection
for pessimists. Although these results may differ depending on the
responder, application area, and decision criteria, the output obtained
from the proposed decision-making model and approach might be helpful to
general construction engineers in judging the relative importance of
various criteria and alternatives specified in this research.
Appendix: List of Relative Importance on Each Criterion
1. Relative importance of [C.sub.1], [C.sub.2], [C.sub.3],
[C.sub.4], [C.sub.5], and [C.sub.6]
[C.sub.1] [C.sub.2] [C.sub.3] [C.sub.4]
[C.sub.1] EE EI TL LE
[C.sub.2] EI EE VL TL
[C.sub.3] TS VS EE SE
[C.sub.4] SE TS LE EE
[C.sub.5] LE LE VL VL
[C.sub.6] EI EI TL LE
[C.sub.5] [C.sub.6]
[C.sub.1] SE EI
[C.sub.2] SE EI
[C.sub.3] VS TS
[C.sub.4] VS SE
[C.sub.5] EE LE
[C.sub.6] SE EE
2. In cost factor ([C.sub.1]), relative importance
of [C.sub.11], [C.sub.12], and [C.sub.13]
[C.sub.11] [C.sub.12] [C.sub.13]
[C.sub.11] EE VL LE
[C.sub.12] VS EE SE
[C.sub.13] SE LE EE
3. In performance factor ([C.sub.2]),
relative importance of [C.sub.21]
and [C.sub.22]
[C.sub.21] [C.sub.22]
[C.sub.21] EE LE
[C.sub.22] SE EE
4. In flexibility factor ([C.sub.3]),
relative importance of [C.sub.31] and
[C.sub.32]
[C.sub.31] [C.sub.32]
[C.sub.31] EE EI
[C.sub.32] EI EE
5. In interference factor ([C.sub.4]),
relative importance of [C.sub.41] and
[C.sub.42]
[C.sub.41] [C.sub.42]
[C.sub.41] EE EI
[C.sub.42] EI EE
6. In maintenance factor ([C.sub.5]), relative
importance of [C.sub.51], [C.sub.52], and
[C.sub.53]
[C.sub.51] [C.sub.52] [C.sub.53]
[C.sub.51] EE LE TL
[C.sub.52] SE EE TL
[C.sub.53] TS TS EE
7. In practicability factor ([C.sub.6]),
relative importance of [C.sub.61] and
[C.sub.62]
[C.sub.61] [C.sub.62]
[C.sub.61] EE TL
[C.sub.62] TS EE
8. In network flexibility factor ([C.sub.31]), relative
importance of [C.sub.311], [C.sub.312], and [C.sub.313]
[C.sub.311] [C.sub.312] [C.sub.313]
[C.sub.311] EE TS VS
[C.sub.312] TL EE SE
[C.sub.313] VL LE EE
9. In applicability factor ([C.sub.62]), relative importance of
[C.sub.621] [C.sub.622], [C.sub.623], and [C.sub.524]
[C.sub.621] [C.sub.622] [C.sub.623] [C.sub.624]
[C.sub.621] EE TL LE LE
[C.sub.622] TS EE EI EI
[C.sub.623] SE EI EE SE
[C.sub.624] SE EI LE EE
10. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.11]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE VL LE EI
[A.sub.2] VS EE SE EI
[A.sub.3] SE LE EE TS
[A.sub.4] EI EI TL EE
[A.sub.5] SE LE EI TS
[A.sub.5]
[A.sub.1] LE
[A.sub.2] SE
[A.sub.3] EI
[A.sub.4] TL
[A.sub.5] EE
11. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.12]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE SE EI EI
[A.sub.2] LE EE TS VS
[A.sub.3] EI TL EE VS
[A.sub.4] EI VL VL EE
[A.sub.5] VL VL EI TS
[A.sub.5]
[A.sub.1] VS
[A.sub.2] VS
[A.sub.3] EI
[A.sub.4] TL
[A.sub.5] EE
12. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.13]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE LE TL VL
[A.sub.2] SE EE LE TL
[A.sub.3] TS SE EE LE
[A.sub.4] VS TS SE EE
[A.sub.5] VS VS VS SE
[A.sub.5]
[A.sub.1] VL
[A.sub.2] VL
[A.sub.3] VL
[A.sub.4] LE
[A.sub.5] EE
13. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.21]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE VS EI LE
[A.sub.2] VL EE LE VL
[A.sub.3] EI SE EE LE
[A.sub.4] SE VS SE EE
[A.sub.5] LE SE EI TL
[A.sub.5]
[A.sub.1] SE
[A.sub.2] LE
[A.sub.3] EI
[A.sub.4] TS
[A.sub.5] EE
14. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.22]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE VS VS SE
[A.sub.2] VL EE SE LE
[A.sub.3] VL LE EE VL
[A.sub.4] LE SE VS EE
[A.sub.5] VL EI EI TL
[A.sub.5]
[A.sub.1] VS
[A.sub.2] EI
[A.sub.3] EI
[A.sub.4] TS
[A.sub.5] EE
15. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.311]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE VS EI LE
[A.sub.2] VL EE LE TL
[A.sub.3] EI SE EE LE
[A.sub.4] SE TS SE EE
[A.sub.5] SE VS SE SE
[A.sub.5]
[A.sub.1] LE
[A.sub.2] VL
[A.sub.3] LE
[A.sub.4] LE
[A.sub.5] EE
16. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.312]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE VS VS TS
[A.sub.2] VL EE SE LE
[A.sub.3] VL LE EE TL
[A.sub.4] TL SE TS EE
[A.sub.5] TL SE TS EI
[A.sub.5]
[A.sub.1] TS
[A.sub.2] LE
[A.sub.3] TL
[A.sub.4] EI
[A.sub.5] EE
17. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.313]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE SE VS VS
[A.sub.2] LE EE TS VS
[A.sub.3] VL TL EE TS
[A.sub.4] VL VL TL EE
[A.sub.5] VL TL EI SE
[A.sub.5]
[A.sub.1] VS
[A.sub.2] TS
[A.sub.3] EI
[A.sub.4] LE
[A.sub.5] EE
18. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.32]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE TS VS VS
[A.sub.2] TL EE VS SE
[A.sub.3] VL VL EE LE
[A.sub.4] VL LE SE EE
[A.sub.5] LE EI TS SE
[A.sub.5]
[A.sub.1] SE
[A.sub.2] EI
[A.sub.3] TL
[A.sub.4] LE
[A.sub.5] EE
19. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.41]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE TS VS VS
[A.sub.2] TL EE SE SE
[A.sub.3] VL LE EE EI
[A.sub.4] VL LE EI EE
[A.sub.5] VL VL LE LE
[A.sub.5]
[A.sub.1] VS
[A.sub.2] VS
[A.sub.3] SE
[A.sub.4] SE
[A.sub.5] EE
20. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.42]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE SE VS SE
[A.sub.2] LE EE TS EI
[A.sub.3] VL TL EE TL
[A.sub.4] LE EI TS EE
[A.sub.5] LE EI TS EI
[A.sub.5]
[A.sub.1] SE
[A.sub.2] EI
[A.sub.3] TL
[A.sub.4] EI
[A.sub.5] EE
21. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.51]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE SE EI LE
[A.sub.2] LE EE EI TL
[A.sub.3] EI EI EE LE
[A.sub.4] SE TS SE EE
[A.sub.5] TS VS TS EI
[A.sub.5]
[A.sub.1] TL
[A.sub.2] VL
[A.sub.3] TL
[A.sub.4] EI
[A.sub.5] EE
22. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.52]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE TS SE LE
[A.sub.2] TL EE LE TL
[A.sub.3] LE SE EE LE
[A.sub.4] SE TS SE EE
[A.sub.5] TS VS TS SE
[A.sub.5]
[A.sub.1] TL
[A.sub.2] VL
[A.sub.3] TL
[A.sub.4] LE
[A.sub.5] EE
23. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.53]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE TL VL LE
[A.sub.2] TS EE LE SE
[A.sub.3] VS SE EE VS
[A.sub.4] SE LE VL EE
[A.sub.5] TS EI LE SE
[A.sub.5]
[A.sub.1] TL
[A.sub.2] EI
[A.sub.3] SE
[A.sub.4] LE
[A.sub.5] EE
24. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.61]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE LE EI EI
[A.sub.2] SE EE TS EI
[A.sub.3] EI TL EE LE
[A.sub.4] EI EI SE EE
[A.sub.5] TS EI VS SE
[A.sub.5]
[A.sub.1] TL
[A.sub.2] EI
[A.sub.3] VL
[A.sub.4] LE
[A.sub.5] EE
25. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.621]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE LE EI LE
[A.sub.2] SE EE SE EI
[A.sub.3] EI LE EE LE
[A.sub.4] SE EI SE EE
[A.sub.5] VS SE TS SE
[A.sub.5]
[A.sub.1] VL
[A.sub.2] LE
[A.sub.3] TL
[A.sub.4] LE
[A.sub.5] EE
26. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.622]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE TL VL TL
[A.sub.2] TS EE LE EI
[A.sub.3] VS SE EE SE
[A.sub.4] TS EI LE EE
[A.sub.5] VS SE EI SE
[A.sub.5]
[A.sub.1] VL
[A.sub.2] LE
[A.sub.3] EI
[A.sub.4] LE
[A.sub.5] EE
27. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.623]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE SE VS TS
[A.sub.2] LE EE TS EI
[A.sub.3] VL TL EE LE
[A.sub.4] TL EI SE EE
[A.sub.5] EI TS VS TS
[A.sub.5]
[A.sub.1] EI
[A.sub.2] TL
[A.sub.3] VL
[A.sub.4] TL
[A.sub.5] EE
28. Relative importance of [A.sub.1], [A.sub.2], [A.sub.3],
[A.sub.4], and [A.sub.5] based on [C.sub.624]
[A.sub.1] [A.sub.2] [A.sub.3] [A.sub.4]
[A.sub.1] EE LE TL LE
[A.sub.2] SE EE EI EI
[A.sub.3] TS EI EE SE
[A.sub.4] SE EI LE EE
[A.sub.5] VS SE EI SE
[A.sub.5]
[A.sub.1] VL
[A.sub.2] LE
[A.sub.3] EI
[A.sub.4] LE
[A.sub.5] EE
doi.org/10.3846/13923730.2011.652157
Acknowledgement
This research was supported by the Yeungnam University research
grants in 2009.
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Shaohua Jiang (1), Won-Suk Jang (2), Miroslaw J. Skibniewski (3)
(1) Faculty of Infrastructure Engineering, Dalian University of
Technology, No. 2 Linggong Road, Ganjingzi District, Dalian City,
Liaoning Province, P. R. C., 116024, China
(2) Civil Engineering Department, Yeungnam University, 214-1
Dae-Dong Gyeongsan-Si Gyeongsangbuk-Do 712-749, South Korea
(3) Department of Civil and Environmental Engineering, University
of Maryland, College Park, MD 20742, USA
E-mails: (1)
[email protected]; (2)
[email protected]
(corresponding author); (3)
[email protected]
Received 09 Jan. 2011; accepted 28 Apr. 2011
Shaohua JIANG. Dr, Full time Assistant Professor in the Division of
Construction Management, faculty of Infrastructure Engineering at Dalian
University of Technology, China. His research interests include
long-distance tracking system, building information and knowledge
management, sustainability.
Won-Suk JANG. Dr, Full time Assistant Professor in the Department
of Civil Engineering, College of Engineering at Yeungnam University,
South Korea. Member of Korean Society of Civil Engineers (KSCE), Korea
Institute of Construction Engineering and Management (KICEM), Korean
Society of Ubiquitous Monitoring (KSUM), and Korean Institute of
Building Information Modeling (KIBIM). His research interests include
IT-based Civil and Infrastructure Engineering and Management, such as
applications of wireless sensor network, web-based project management
systems, and construction assets tracking, and building information
modeling.
Mirostaw J. SKIBNIEWSKI. Dr, A. James Clark Endowed Chair Professor
of Construction Engineering and Project Management in the Department of
Civil and Environmental Engineering at the University of Maryland in
College Park, USA. Member of American Society of Civil Engineers (ASCE);
a founding member, co-director and past president of International
Association for Automation and Robotics in Construction (IAARC); and an
affiliate of International Council for Building Research Studies and
Documentation (CIB). His research interests include construction
automation and robotics, information technology in construction,
e-commerce technology applications in construction, and green
intelligent buildings.
Table 1. Linguistic expression of triangular fuzzy scale and
reciprocal scale
Linguistic Expression Triangular Triangular fuzzy
Fuzzy Scale reciprocal scale
Very Small (VS) (1/4, 1/4, 1/3) (3, 4, 4)
Too Small (TS) (1/4, 1/3, 1/2) (2, 3, 4)
Smaller than Equal (SE) (1/3, 1/2, 1) (1, 2, 3)
Equally Important (EI) (1/2, 1, 2) (1/2, 1, 2)
Exactly Equal (EE) (1, 1, 1) (1, 1, 1)
Equally Important (EI) (1/2, 1, 2) (1/2, 1, 2)
Larger than Equal (LE) (1, 2, 3) (1/3, 1/2, 1)
Too Large (TL) (2, 3, 4) (1/4, 1/3, 1/2)
Very Large (VL) (3, 4, 4) (1/4, 1/4, 1/3)
Table 2. Hierarchy of decision criteria and sub-criteria
and description
Main Criteria Sub-Criteria Bottom-Criteria
Cost Device Cost
([C.sub.1]) ([C.sub.11])
Installation
Cost
([C.sub.12])
Maintenance
Cost
([C.sub.13])
Performance Accuracy
([C.sub.2]) ([C.sub.21])
Data Rate
([C.sub.22])
Flexibility Networking Coverage Range
([C.sub.3]) ([C.sub.31]) ([C.sub.311])
Communication
Efficiency
([C.sub.312])
Topology
([C.sub.313])
Interoperability
([C.sub.32])
Interference Co-existence
([C.sub.4]) ([C.sub.41])
Obstruction
([C.sub.42])
Maintenance Labor Use
([C.sub.5]) ([C.sub.51])
Robustness
([C.sub.52])
Power Consumption
([C.sub.53])
Practicability Ease of Use
([C.sub.6]) ([C.sub.61])
Applicability Post Processing
([C.sub.62]) ([C.sub.621])
Device Size
(C[6.sub.22])
Commercial
Availability
([C.sub.623])
Attachment
([C.sub.624])
Main Criteria Description
Cost A cost for
([C.sub.1]) purchase
at the
beginning
stage of
construction.
A cost needed
for the
device
installation
and settings.
Long-term
maintenance
cost of the
device during
construction
process.
Performance Positioning
([C.sub.2]) accuracy for
tracking and
monitoring
the device
attached in
materials.
Amount of data
per second
transmitted
from the device.
Flexibility Maximum
([C.sub.3]) Transmitter-
Receiver
separation
distance for
wireless
communication.
Received
signal strength,
link quality,
data reception
rate,
communication
performance.
Expandability
and geographic
scalability for
mesh networking.
Interoperability
with other types
of device.
Interference Wireless
([C.sub.4]) interference
from similar
range of
bandwidth or
frequency.
Wireless
interference
from construction
materials, built
structure, or
equipment.
Maintenance Amount
([C.sub.5]) of labor hour
and efforts
required for
device
maintenance.
Level of survival
under harsh
environment,
such as rain,
humidity, or
temperature.
Power needed
for the device
to be operated
with limited
battery condition.
Practicability Level of easiness
([C.sub.6]) to operate the
device for general
construction
engineers.
Time and efforts
required to
conduct post data
processing after
data collection.
Unit device size
fitted to the
construction
materials for
tracking and
monitoring.
Commercial
products
available
in the general
market.
Level of
attachment to
the typical
construction
materials.
Table 3. Geometric row mean and fuzzy weights for each
criterion
Criteria Geometric Row Mean Fuzzy Weight
(0.74, 1.20, 1.91) (0.07, 0.17, 0.42)
[C.sub.2] (0.89, 1.35, 2.00) (0.09, 0.19, 0.44)
[C.sub.3] (0.33, 0.39, 0.55) (0.03, 0.06, 0.12)
[C.sub.4] (0.44, 0.59, 0.89) (0.04, 0.08, 0.19)
[C.sub.5] (1.44, 2.24, 2.75) (0.14, 0.32, 0.60)
[C.sub.6] (0.74, 1.20, 1.91) (0.07, 0.17, 0.42)
[C.sub.11] (1.44, 2.00, 2.29) (0.33, 0.57, 0.89)
[C.sub.12] (0.44, 0.50, 0.69) (0.10, 0.14, 0.27)
[C.sub.13] (0.69, 1.00, 1.44) (0.16, 0.29, 0.56)
[C.sub.21] (1.00, 1.41, 1.73) (0.37, 0.67, 1.10)
[C.sub.22] (0.58, 0.71, 1.00) (0.21, 0.33, 0.63)
[C.sub.31] (0.71, 1.00, 1.41) (0.25, 0.50, 1.00)
[C.sub.32] (0.71, 1.00, 1.41) (0.25, 0.50, 1.00)
[C.sub.41] (0.71, 1.00, 1.41) (0.25, 0.50, 1.00)
[C.sub.42] (0.71, 1.00, 1.41) (0.25, 0.50, 1.00)
[C.sub.51] (1.26, 1.82, 2.29) (0.28, 0.53, 0.90)
[C.sub.52] (0.87, 1.14, 1.59) (0.19, 0.33, 0.63)
[C.sub.53] (0.40, 0.48, 0.63) (0.09, 0.14, 0.25)
[C.sub.61] (1.41, 1.73, 2.00) (0.52, 0.75, 1.04)
[C.sub.62] (0.50, 0.58, 0.71) (0.18, 0.25, 0.37)
[C.sub.311] (0.40, 0.44, 0.55) (0.09, 0.12, 0.20)
[C.sub.312] (0.87, 1.14, 1.59) (0.20, 0.32, 0.59)
[C.sub.313] (1.44, 2.00, 2.29) (0.33, 0.56, 0.84)
[C.sub.621] (1.19, 1.86, 2.45) (0.19, 0.43, 0.87)
[C.sub.622] (0.50, 0.76, 1.19) (0.08, 0.18, 0.42)
[C.sub.623] (0.49, 0.71, 1.19) (0.08, 0.16, 0.42)
[C.sub.624] (0.64, 1.00, 1.57) (0.10, 0.23, 0.56)
Table 4. Fuzzy weights for each alternative based on
each level of criterion
Criteria [A.sub.1] [A.sub.2]
[C.sub.11] (0.13, 0.32, 0.66) (0.05, 0.10, 0.26)
[C.sub.12] (0.06, 0.12, 0.26) (0.06, 0.09, 0.17)
[C.sub.13] (0.23, 0.40, 0.64) (0.15, 0.27, 0.48)
[C.sub.21] (0.07, 0.13, 0.30) (0.19, 0.40, 0.70)
[C.sub.22] (0.04, 0.06, 0.13) (0.11, 0.22, 0.46)
[C.sub.311] (0.08, 0.17, 0.36) (0.22, 0.43, 0.71)
[C.sub.312] (0.04, 0.06, 0.12) (0.12, 0.25, 0.49)
[C.sub.313] (0.04, 0.06, 0.11) (0.06, 0.09, 0.17)
[C.sub.32] (0.04, 0.07, 0.13) (0.08, 0.14, 0.28)
[C.sub.41] (0.04, 0.06, 0.11) (0.07, 0.12, 0.25)
[C.sub.42] (0.05, 0.09, 0.20) (0.07, 0.16, 0.36)
[C.sub.51] (0.10, 0.22, 0.51) (0.15, 0.33, 0.67)
[C.sub.52] (0.09, 0.17, 0.35) (0.20, 0.40, 0.70)
[C.sub.53] (0.20, 0.40, 0.70) (0.07, 0.14, 0.31)
[C.sub.61] (0.11, 0.26, 0.60) (0.06, 0.13, 0.32)
[C.sub.621] (0.13, 0.31, 0.66) (0.07, 0.16, 0.40)
[C.sub.622] (0.26, 0.45, 0.72) (0.08, 0.18, 0.37)
[C.sub.623] (0.05, 0.09, 0.19) (0.09, 0.19, 0.39)
[C.sub.624] (0.17, 0.39, 0.75) (0.07, 0.18, 0.45)
Criteria [A.sub.3] [A.sub.4]
[C.sub.11] (0.07, 0.15, 0.35) (0.12, 0.28, 0.64)
[C.sub.12] (0.09, 0.17, 0.34) (0.21, 0.38, 0.64)
[C.sub.13] (0.10, 0.17, 0.32) (0.06, 0.10, 0.19)
[C.sub.21] (0.08, 0.17, 0.43) (0.05, 0.08, 0.18)
[C.sub.22] (0.17, 0.34, 0.61) (0.06, 0.10, 0.21)
[C.sub.311] (0.09, 0.20, 0.44) (0.06, 0.12, 0.27)
[C.sub.312] (0.20, 0.39, 0.68) (0.08, 0.15, 0.31)
[C.sub.313] (0.12, 0.21, 0.39) (0.23, 0.40, 0.63)
[C.sub.32] (0.22, 0.41, 0.67) (0.13, 0.25, 0.48)
[C.sub.41] (0.11, 0.22, 0.45) (0.11, 0.22, 0.45)
[C.sub.42] (0.23, 0.44, 0.76) (0.07, 0.16, 0.36)
[C.sub.51] (0.11, 0.25, 0.58) (0.05, 0.11, 0.27)
[C.sub.52] (0.11, 0.24, 0.50) (0.06, 0.12, 0.26)
[C.sub.53] (0.05, 0.07, 0.16) (0.12, 0.26, 0.50)
[C.sub.61] (0.15, 0.34, 0.69) (0.07, 0.18, 0.46)
[C.sub.621] (0.12, 0.29, 0.66) (0.07, 0.16, 0.40)
[C.sub.622] (0.05, 0.10, 0.22) (0.08, 0.18, 0.37)
[C.sub.623] (0.22, 0.41, 0.67) (0.12, 0.22, 0.47)
[C.sub.624] (0.06, 0.12, 0.32) (0.09, 0.21, 0.49)
Criteria [A.sub.5]
[C.sub.11] (0.07, 0.15, 0.35)
[C.sub.12] (0.14, 0.25, 0.42)
[C.sub.13] (0.04, 0.06, 0.11)
[C.sub.21] (0.10, 0.22, 0.49)
[C.sub.22] (0.13, 0.28, 0.56)
[C.sub.311] (0.05, 0.09, 0.20)
[C.sub.312] (0.08, 0.15, 0.31)
[C.sub.313] (0.13, 0.23, 0.44)
[C.sub.32] (0.07, 0.13, 0.29)
[C.sub.41] (0.19, 0.38, 0.65)
[C.sub.42] (0.07, 0.16, 0.36)
[C.sub.51] (0.05, 0.09, 0.19)
[C.sub.52] (0.04, 0.07, 0.15)
[C.sub.53] (0.07, 0.14, 0.31)
[C.sub.61] (0.05, 0.10, 0.22)
[C.sub.621] (0.04, 0.08, 0.20)
[C.sub.622] (0.05, 0.10, 0.22)
[C.sub.623] (0.05, 0.08, 0.16)
[C.sub.624] (0.05, 0.10, 0.25)
Table 5. Ranking values from the FAI on five alternatives
according to the decision maker's attitude (k = 1 for
optimist, k = 0 for pessimist, and k = 0.5 for neutral
person)
Membership Membership Membership
Alternative function for function for function for
k = 1 (rank) k = 0 (rank) k = 0.5
(rank)
RFID 0.478 (3) 0.915 (3) 0.696 (3)
([A.sub.1])
GPS 0.508 (2) 0.900 (4) 0.704 (2)
([A.sub.2])
WI-FI 0.530 (1) 0.899 (5) 0.714 (1)
([A.sub.3])
Zigbee 0.439 (4) 0.933 (2) 0.686 (4)
([A.sub.4])
UWB 0.399 (5) 0.941 (1) 0.670 (5)
([A.sub.5])
