When construction materials arrive at CIC job sites, they
are identified at the unloading area, and the job site inventory database in
the central computer is updated. CIC requires tight control on inventory and
integrated operation of automated equipment.
Further, all construction materials must be tracked from the
time of their arrival at the job site to their final position in the finished
facility. Such tracking of construction materials may be done by employing
automated identification systems.
There are two means of tracking construction materials:
direct and indirect. Direct tracking involves identifying a construction
material by a unique code on its surface. This method of tracking can be
employed with the use of large prefabricated components.
Indirect tracking involves identifying construction material
by a unique code on the material handling equipment. This method of tracking
can be employed for tracking bulk materials such as paints [Rembold et al.,
1985]. Select automatic identification systems for construction materials are
described below.
Bar Coding
The U.S. Department of Defense (DOD) was the first
organization to implement bar coding technology. The Joint Steering Group for
Logistics Applications of Automated Marking and Reading Symbols (LOGMARS)
spearheaded the DOD’s effort in the implementation of bar coding technology.
The symbology of bar codes conveys information through the placement of wide or
narrow dark bars that create narrow or wide white bars.
With the rise of the LOGMARS project, code 39 (also called
“3 of 9” coding) has become a standard for bar coding. To date, most
construction bar code applications have used the code 39 symbology [Teicholz
and Orr, 1987; Bell and McCullough, 1988].
Laser beams and magnetic foil code readers are two basic
technologies available for reading bar codes. Lasers offer the ability to read
bar codes that move rapidly. Magnetic code readers are among the most reliable
identification systems. It is possible to transmit the code without direct
contact between the code reader and the write head on the code carrier. When
the workpiece passes the read head, the code is identified by the code reader
[Teicholz and Orr, 1987; Rembold et al., 1985].
Voice Recognition
Voice recognition provides computers the capability of
recognizing spoken words, translating them into character strings, and sending
these strings to the central processing unit (CPU) of a computer. The objective
of voice recognition is to obtain an input pattern of voice waveforms and
classify it as one of a set of words, phrases, or sentences.
This requires two steps: (1) analyze the voice signal to
extract certain features and characteristics sequentially in time and (2)
compare the sequence of features with the machine knowledge of a voice, and
apply a decision rule to arrive at a transcription of the spoken command
[Stukhart and Berry, 1992].
Vision Systems
A vision system takes a two-dimensional picture by either
the vector or the matrix method. The picture is divided into individual grid
elements called pixels. From the varying gray levels of these pixels, the
binary information needed for determining the picture parameters is extracted.
This information allows the system, in essence, to see and recognize objects.
The vector method is the only method that yields a high
picture resolution with currently available cameras. The vector method involves
taking picture vectors of the scanned object and storing them at constant time
intervals. After the entire cycle is completed, a preprocessor evaluates the
recomposed picture information and extracts the parameters of interest [Rembold
et al., 1985].
