A diagnostic expert system to overcome construction problems in rigid highway pavement.
Mosa, Ahmed Mancy ; Taha, Mohd Raihan ; Ismail, Amiruddin 等
Introduction
Rapid urbanization and increase in population create a high demand
for infrastructure to transport products, raw materials, and people
faster and safer between localities (Mulungye et al. 2007; Syamsunur et
al. 2011). Construction of a highway system as a part of public
infrastructure is a significant way for any country to improve and boost
economy (Chou, Tseng 2011; Mulungye et al. 2007; Santos et al. 2010).
During the construction of rigid pavements, highway engineers face
many problems that they must decide the controlling measures. The
expected problems range from mildly disruptive to financially
catastrophic. Generally, these problems affect the quality of the
construction and increase initial cost. Experts can control and solve
these problems using their tacit knowledge (Miller et al. 2007).
However, novice engineers cannot overcome such problems, suggest
suitable solutions, avoid their causes, and prevent the same problems
from occurring in other parts of the work. Transfer of expertise from
experts to novices is difficult in the domain of construction (Persson,
Landin 2007). Therefore, there is a need for a system in which experts
could share their experience with other engineers both during a project
and afterward. If no such transfer of expertise and knowledge occurs,
the novices may repeat the mistakes that the experts have learned how to
avoid (Persson, Landin 2007). Documentation, classification and
computerization of these problems, their causes, solutions, and
preventive actions can be very helpful for controlling and preventing
them.
This paper describes the development of an expert system that can
be used by novice engineers on rigid highway pavement construction sites
to control the problems that they encounter. The system can also be used
as an instructional tool for interested highway engineers. In addition,
the system can archive and organize raw knowledge from experts for use
by all engineers working in this field. Domain experts can use the
system to share experiences. The proposed system is supported by a
Geographic Information System (GIS) to provide location, weather, and
traffic information.
Expert systems are used to overcome problems in fields from
assessment to engineering consulting (Ruiz-Mezcua et al. 2011) by
employing human expertise in different domains (Qian et al. 2005).
Expert systems can be used in any field (Ooshaksaraie et al. 2012).
Therefore, using expert systems to solve sophisticated engineering
problems that require a significant amount of experience is very common,
and researchers have developed many systems for use in different fields
(Park et al. 2010).
In highway construction industry, many expert systems were
developed in the domain of design and maintenance and rehabilitation,
but to author's knowledge, no such systems were developed for
pavement construction.
In design stage, Syamsunur et al. (2011) developed an expert system
for route selection that uses GIS techniques and opinions of human
experts. This system could be considered as a new version of the system
developed by Mohsen and Crower (1991). Teh et al. (2005) developed an
expert system named RC-MMS, which supports designers in material
selection for highway pavements (Teh et al. 2005). This system is
similar to the system developed by Hozayen and Haas (1992). Deprizon
developed an expert system to assist engineers in the structural design
of flexible highway pavements. The system uses the American Association
for State Highways and Transportation Officials (AASHTO) standards and
human expertise acquired from domain experts to calculate the minimum
thickness of the layers of flexible highway pavements (Deprizon et al.
2009). Goh (1993) developed an advisory expert system to simulate the
designer procedure in design of structural layers of asphalt pavements
based on the available information of materials, traffic and area
conditions. Khedr and Mikhail (1996) developed an expert system named
EXPAVE to provide assistance to the novice designers in flexible
pavements during the design stage. EXPAVE is also capable of predicting
the structural performance of the existing pavements and enable
designing of the overlays (Khedr, Mikhail 1996).
In the domain of pavement management, Kaur and Tekkedil (2000)
developed a fuzzy expert system, which employs the information about the
materials used, thickness of the flexible pavement layers, traffic
characteristics, and road age to predict the pavement performance in the
form of rut depth. Kuprenas et al. (1995) developed a forward-chaining
rule-based expert system to identify failure causes in flexible
pavements. Tsao et al. (1994) developed a vision expert system to
diagnose the distresses in rigid highway pavements without human
interaction based on the images of the distresses. Lan et al. (1993)
developed an expert system named PDS (Pavement Distress System) to
diagnose distresses of flexible pavement based on the expertise of
Taiwanese experts. Other expert systems were developed in the domain of
pavement maintenance and rehabilitation management as classified by
Ismail (2009a, b).
The current study presents the development stages of the ES-CCPRHP,
which is an expert system that controls construction problems in rigid
highway pavements. Prior to the development process, the need for such a
system was determined from a literature review and validated by a
questionnaire survey. The first stage involved knowledge acquisition
from written sources and from domain experts via interviews and
questionnaires. The acquired knowledge was classified under the
supervision of domain experts, after which the classified knowledge was
represented in the form of rules. The third stage involved building the
system by coding the rules using Visual Basic. In the fourth stage, the
system was verified and validated by unit and integrated testing, user
satisfaction testing, behavior comparison between the system and
experts, and case study. The developed system can be used with
confidence and easily updated.
1. Rigid pavement construction
Construction of rigid pavements process is sophisticated, and
involves many activities like concrete producing, placing, spreading,
compacting, finishing, texturing, curing, protecting, jointing, testing,
and other sub-activities. In addition, this process is performed over
large areas, outdoors, and under miscellaneous conditions. Therefore,
problems are very usual during construction (O' Flaherty 1988;
Oglesby, Hicks 1982).
In fact, the same materials, which are, cement, sand, aggregate,
water and reinforcement, are used to prepare the mix which is used in
the construction of rigid pavements. The obtained slab sometimes behaves
and looks good, and other times that same slab can exhibit bad
qualities. There is no simple answer to explain these differences in the
obtained products, but major contributing factors that can be summarized
as follows (ACI Committee 304R-00 2000; ACI Committee 306.1-90 2002; ACI
Committee 305.1-06 2007):
1) Over the years, the engineers and contractors have had to deal
with major changes in methods of construction. There is ever increasing
difficulty to find staff with the necessary skill and experience able to
carry out concreting operations on site. Computerized design methods
move the engineers even further from practical realities. It is still
easier to assess and test material products than its construction
practices.
2) Most of concrete today is manufactured off the job site and away
from the direct control of the resident engineer (which is a ready mixed
concrete). This has produced another contractual and communications
interface, where misunderstandings and mistakes may occur.
3) The aspect of environmental protection and energy saving has led
to greater planning restriction on cement and aggregate material
processing and pressed to use by-products such as pulverized fuel ash,
ground granulated slag and silica fume, often of unknown and variable
quality. Again, the lack of experience, proper understanding, and
control over the use of these materials can lead to problems.
4) Adverse climatic conditions may influence the construction
operation and then affect the quality desired.
5) Bad preparation and scheduling at the site can cause
interruption among the construction operation stages.
The experts can manage the operation and overcome the problems
faced during construction, but the novice engineers need support to do
that.
Cracking is one of the major problems encountered in rigid
pavements. Different types of cracks are very common in highway
pavements. Crack is a complete or incomplete separation of concrete into
two or more parts produced by breaking or fracturing (ACI Committee
116R-00 2000). Cracking can be classified into plastic concrete cracking
and cracking of hardened concrete. Cracking of plastic concrete involves
cracks occurring at the surface of fresh concrete during the interval
after concrete placing (when it is possible to be re-moulded) and before
concrete hardening (ACI Committee 116R-00 2000). This interval may range
from 1 hour to 12 hours depending on air temperature, water content in
mixture, and use of accelerators or retarders in mixture. On the other
hand, cracking of hardened concrete occurs due to shrinkage of concrete.
Problems involved in this pattern of cracking could be considered as a
construction problem if they occur before opening the road for the
traffic.
Restraint of pavement can cause cracking in two patterns that are
cracks within slab and cracks near joints. Cracking includes the
following types:
1) Diagonal shallow cracks;
2) Craze cracks;
3) Transverse and oblique cracks;
4) Longitudinal cracks;
5) Corner cracks (D-cracks);
6) Transverse and diagonal cracks at transverse joints;
7) Longitudinal cracks at longitudinal joints;
8) Cracks at the intersection of joints.
Table 1 abstracts the causes of the crack generation.
2. The need for the proposed system
Based on the literature review and the concepts explained in
existing research, there is a serious need for an expert system in the
domain under study. This conclusion was tested with a questionnaire
survey. This is a new approach in comparison with other studies in this
area. Other researchers have depended on the literature to establish the
need for their proposed expert system. After developing their system,
they use a questionnaire to check if it is needed. The present approach
has two major advantages over the approach used by other researchers.
First, ensuring that there is a real need for the proposed system before
creating it will avoid wasting time, money, and effort on developing a
system that is not required. Second, the domain of the study can be
specified depending on the comments of questionnaire respondents.
The questionnaires were submitted to 30 highway engineers who had
different levels of experience. The engineers were divided into two
groups. The first group included eight engineers who had between 5 and 9
years of experience and two experts with more than 20 years of
experience. The second group included 20 engineers who had 3 years of
experience or less. The questionnaire consists of two parts and a total
of seven questions. Of these, five questions ask about the significance
of the proposed system and its role in the study domain, and the other
two questions ask about the number of domain experts. Because a Likert
scale is a very effective way to evaluate the results of a questionnaire
(Gob et al. 2007; Lee et al. 2010), the questions are evaluated on
5-point Likert scale, where the value 1 represents "strongly
disagree" and the value 5 represents "strongly agree".
The results, summarized in Table 2, speak for themselves and reflect the
significance for the proposed system. The results express a significant
concern over the loss of human expertise as experts retire or pass away.
The proposed system can preserve their expertise in a classified form
and can help to educate a new generation of experts. In summary, the
results of the questionnaire validate the conclusion extracted from the
literature, that is, there is high demand for an expert system in the
domain of highway pavement.
3. Developing the system
An expert system uses knowledge instead of data to solve problems.
An expert system development team is led by a knowledge engineer and
includes a domain expert and an end user. The role of the knowledge
engineer is to build the system by acquiring knowledge from written
sources and from the domain expert. During the knowledge acquisition
stage, the knowledge engineer works with the domain expert to acquire,
classify and analyze the knowledge. The knowledge engineer codes the
acquired knowledge in a classified form to construct a computer system
using a programming tool. The constructed system is tested and evaluated
by the end user, who is the third member of the team (Alani et al. 2009;
Raza 2009; Spundak et al. 2010). The proposed expert system in this
study (ES-CCPRHP) has passed through the stages of development
successfully.
Many studies have investigated the integrated usage of expert
systems, which can simulate the performance of a human expert (Ahmadi,
Ebadi 2010), and GIS, which has many uses in decision making (Demircan
et al. 2011; Sikder 2009), in fields such as ecology, agriculture,
forestry, transportation, traffic, public health, and environmental
protection (Wei et al. 2011). GIS can provide information about
location, weather conditions, road traffic, and other data (Durduran
2010; Niaraki, Kim 2009; Sadeghi-Niaraki et al. 2011).
3.1. Knowledge acquisition and representation
Knowledge acquisition represents the most important stage in the
development of an expert system (Raza 2009). It is also complicated and
time-consuming (Mohd. Zain et al. 2005; Ooshaksaraie et al. 2012; Tan et
al. 2010) as knowledge-based systems require specific analytical
approaches (Castellanos et al. 2011). Knowledge acquisition involves
obtaining and classifying expertise from miscellaneous sources (Qian et
al. 2008). Knowledge engineering methodology usually starts with
reviewing written sources, like books, guidelines, manuals, and papers
associated with the problem domain. Further knowledge can be elicited
from domain experts. Then, the collected knowledge can be combined,
studied, and analyzed repeatedly (Negnevitsky 2005).
In the present study, an extensive review of specialized sources is
performed to construct the initial background knowledge and understand
the concepts of rigid pavement construction and the problems that can be
expected to occur during different stages of construction. Through this
review, the initial knowledge base is constructed as the foundation for
the final knowledge base that represents the core of the expert system.
This initial knowledge is analyzed repeatedly to refine it and limit the
domain of the study. The review focuses on description, causes, and
prevention of problems, instantaneous solutions to problems that occur,
and the possible effects of problems that are not avoided or controlled.
After construction of the initial knowledge base through a literature
review, domain experts were consulted about their knowledge. The experts
obtained their domain knowledge gradually through education and
experience. Selection of domain experts is very significant in any
elicitation expertise. Criteria for choosing domain experts ensure
elicitation of correct expert knowledge. There are two major criteria
for domain experts. The first is the length of experience in the domain,
which affects the judgment and analytical behavior of the expert. The
second can be represented in circumstances in which the expertise is
obtained, which could be theoretical, practical, or a combination of
both (Osuagwu, Okafor 2010). Depending on these criteria, a set of four
experts was selected for the human expertise stage in the present study.
The selected experts are well known and have broad experience in the
domain of rigid pavement construction, as illustrated in Table 3.
Another set of experts is selected to participate in the evaluation
stage. Knowledge elicitation involves obtaining knowledge from experts
to understand how they make decisions. This goal can be achieved by
methods such as interviews (Tan et al. 2010). Experts can also be
observed as they work to identify implicit knowledge (Castellanos et al.
2011; Tan et al. 2010). The knowledge engineer can decide on which
method to use depending on the study domain, amount of knowledge needed,
and the efforts required to analyze the collected information (Osuagwu,
Okafor 2010). In this paper, expertise was elicited from the selected
experts by unstructured interviews, structured interviews, and
questionnaires. Unstructured interviews were held with the experts to
gain a general understanding of their practical experience with domain
problems and to build a friendly relationship with them to simplify the
process of expertise elicitation. Problems in the study domain were
discussed in general, and a few problems were discussed specifically.
Each expert referred to some of his practical experiences with rigid
pavement construction problems and the way he dealt with such problems
in the field. During the unstructured interviews, only a few questions
were asked of the experts, but the experts gave detailed responses to
these questions. After each unstructured interview, the knowledge was
reanalyzed, reclassified, and updated in preparation for structured
interviews, which represent the next step in expertise elicitation.
During structured interviews, a specific aspect of the domain is
emphasized in each interview to ensure robust results. The primary
knowledge that is collected through expertise elicitation is much more
than the secondary knowledge obtained from the literature, because the
experts do not document their experiences. The acquired knowledge was
abstracted in questionnaire form and submitted to the experts to
complete the knowledge acquisition stage. A questionnaire can simplify
the process of knowledge elicitation from experts (Ma et al. 2011)
because it gives the expert time to think about his response before
answering. Questionnaires are an effective way to elicit knowledge as
they can save time, money, and effort, especially when the knowledge
engineer knows exactly the required knowledge characteristics (Rezaei et
al. 2011). In addition, the classified form of the questionnaire
simplifies the mission of the expert because he can review each problem
separately. Blank spaces were provided following each question for the
expert to write his comments. Three experts completed questionnaires,
and only one questionnaire form was not returned. One of the experts
asked for clarification before answering some of the questions. The
experts provided many useful comments on the questionnaire that enriched
the knowledge base. After reviewing and analysis of their answers and
comments in the questionnaire, the experts were interviewed again to
clarify some points in their comments and to focus on some details in
their solutions to the problems. In the final step of the knowledge
acquisition process, the knowledge base is reanalyzed and rearranged in
preparation for final classification.
Through the extensive review and repeated analysis of the acquired
knowledge, the domain problems are classified depending on their forms,
locations, effects, and other common features so that inspectors can
diagnose a problem visually or via tests and measurement results. In
addition, problems' description, likely causes, preventive actions,
instantaneous solutions, and their possible effects if they are not
controlled are stated. The repeated classification stages were discussed
with experts for modification. The final classification was reviewed by
three experts during the focus interviews, and they agreed that it
represented their knowledge well. Figure 1 illustrates the
classification diagram of the titles of the problems.
[FIGURE 1 OMITTED]
The problems are identified depending on their characteristics,
which could be diagnosed visually by their appearance or by measurements
and tests. The name of each problem summarizes its description. Experts
can note such problems easily and quickly make decisions to solve them,
whereas novice engineers cannot.
Avoiding these problems by preventive actions can save time, money,
and effort. Immediate decisions to identify and take preventive measures
must be made to control these problems. Site inspectors have the
authority to reject any load that contains an imperfect concrete. The
decision to reject a load can be based on visual inspection, temperature
measurement, or tests. Accepted loads should be documented in detail,
including their placing, temperature, sample number, slump, and other
remarks.
3.2. Building the system
In this study, an expert system is developed to detect problems and
guide the user through the diagnostic process. Knowledge about these
problems can be represented in the form of rules. Therefore, a
rule-based system can be considered as the most correct option. When
problems arise, experts need to collect information about it and then
make a decision. Therefore, a forward-chaining inference engine, which
is data driven, is suitable for knowledge representation in a rule-based
expert system (Negnevitsky 2005). This procedure works from the facts in
the knowledge base toward the goal or conclusion (Chu et al. 2009;
Spundak et al. 2010). The reasoning originates from the given
information, and then continues forward with that information. This
approach depends on IF-THEN relationships; if the IF condition is
matched in a rule, the action in its THEN part is applied (Negnevitsky
2005). This process can be modelled as IF (condition) THEN (conclusion)
(Cebi et al. 2009; Pfibyl 2010). We created 450 ES-CCPRHP rules, which
simulate the manner in which domain experts think.
The classified knowledge, which represents the core of the proposed
system, is prepared in the form of rules to be coded in a computer
environment. The Microsoft Visual Basic programming language was used to
develop ES-CCPRHP, because this language is a very effective and
flexible tool for software development in the Windows environment. In
addition, Microsoft Visual Basic is suitable for use with GIS
(Ooshaksaraie et al. 2012). The source code version of the developed
system has multiple forms that are connected together in one structure.
Each form includes a number of commands responsible for executing
specific functions in the system. The forms and the commands are given
clear, expressive names that are related to the functions of each
command. In addition, many remarks are included in the coding menus to
simplify the updating process. This version (with the extension.vbp) is
designed for use by the knowledge engineer who is responsible for
developing and updating the system. An executable version (with the
extension. exe) is prepared for use by the end user, who is the highway
engineer, and this version is protected and cannot be edited. Figure 2
illustrates the relationships among the development team and components
of ES-CCPRHP. The system can be operated easily by clicking a button to
execute any step using the mouse or the keyboard; the user can return to
the last step anytime using a Back button, and he can use the Close
button to end the program at any stage. The system is coded in a simple
manner to ensure easy updating by the developer or any other knowledge
engineer in this domain, who is familiar with Visual Basic language.
[FIGURE 2 OMITTED]
3.3. System operation
Similar to any software in the environment of Microsoft Windows,
the system can be run by clicking the icon of the executable file or its
shortcut. The system runs immediately and displays a greeting screen
that includes information about the purpose of the system, the domain
that the system deals with, a brief guide about operating the system,
and three command buttons (CONTINUE, ABOUT, and CLOSE). Users can press
any button using the mouse or keyboard, and then proceed to the next
screen by pressing the CONTINUE button. The next screen presents the
flow chart of domain problems to provide the novices in this domain with
an idea about the problems. This screen also describes the construction
stages, as well as the probable problems and their probable causes as
part of the training process, providing novices a background for moving
on to the next screen. Users can skip this screen by pressing CONTINUE
if they already have sufficient background knowledge or if they are in a
hurry to find solutions. Such a situation usually occurs in problems
encountered during paving operations, including defective concrete
loads, bleeding or cracking of fresh concrete, and paving stoppage due
to bad weather conditions or equipment breakdown. The next screen
includes all the problems that are classified into main and
subcategories of common features. The titles of the categories,
subcategories, and problems simplify descriptions for users. For
example, the cracking category includes two subcategories (cracking of
plastic concrete and cracking of hardened concrete). Cracking of plastic
concrete includes diagonal shallow cracks and craze cracks. The titles
clearly reflect the properties of a problem for simplified diagnosis.
Figure 3 presents the cracking subcategories and specific problems in
one input screen. In addition, the descriptions of each category,
subcategory, and problem can be displayed on separate screens by
pressing the related button. Moreover, pictures that clarify the problem
can be displayed. Videos of test procedures are also provided, which can
be activated with the press of a button. Users can select a category of
problems or one problem or more by selecting an option button or
checking TICK on checkboxes. The system asks a user if the description
presented complies with the description of the problem in the worksite.
When a user confirms by selecting the YES button or checking TICK on the
checkboxes, the system proceeds with the diagnostic process by asking
for more input data, such as test results, field measurements, work
requirements that are documented in project files (e.g. designed
pavement thickness, designed concrete strength, and specified minimum
and maximum values of the tests). The system uses default values when a
user does not answer questions by providing the user multiple options.
For example, when a structural problem occurs in constructed pavement
(thickness and/or strength deficiency), the inference engine will run a
redesign subroutine to evaluate the pavement's structural
properties on the basis of existing input. The system will ask the user
to incorporate some values as explained in Section 3.4.4. One of the
required values in this case is the load transfer coefficient. If a user
does not know this value, the system presents multiple options for the
type of load transfer to help the user select the correct option. In
this example, the options are undo-weled pavement on crushed aggregate
surfacing, doweled pavement on crushed aggregate surfacing, doweled
pavement on hot mix asphalt (without a widened outside lane) and tied
pavement shoulders, continuously reinforced pavement with hot mix
asphalt shoulders, and continuously reinforced concrete pavement with
tied concrete shoulders. When the user selects an option, the system
uses the related default value stored in the working memory.
[FIGURE 3 OMITTED]
The inference engine manipulates the input data and facts in the
working memory in a reasoning process in the knowledge base to match
these data with related rules, draw a conclusion, and display
recommendations. More details can be found in the examples provided in
Section 3.4.4.
A variety of tools, such as labels, texts, option buttons, command
buttons, check boxes, combo boxes, and images, are provided in the forms
to simplify the input and output for the user. These tools are enabled
only when they are necessary for data input; otherwise, they are
disabled (ineffective) to avoid confusing the user. The user interface
displays help and useful details, like explanations about the system,
flow charts, and guides. Users can input the required information by
clicking to choose the offered options, which cover all of the probable
required input data. The system responds immediately by analyzing the
inputs in the inference mechanism to provide solutions.
3.4. System verification and validation
Verification and validation are the most important and difficult
tasks involved in intelligent system development (Aguilar et al. 2008).
Verification can be performed through testing activities to verify that
the correct system is being built. In effective testing, each test
should aim to detect a fault. Each stage and all components of the
system should be tested (Qian et al. 2005). Testing is performed
periodically during the system development process to guarantee that
each activity in the system is performing the intended functions.
ES-CCPRHP is evaluated by a number of test procedures, as explained in
the following sections.
3.4.1. Unit and integration testing
Unit testing means testing the units one by one by separate testing
activities (Aguilar et al. 2008). This testing was performed continually
during all of the stages of development of ES-CCPRHP to verify that each
unit in the system performs the intended function. The internal
structure of the system is checked by the knowledge engineer by covering
all possible combinations of constants, variables, relationships among
them, and paths of the source code. Few mistakes were diagnosed during
the testing process. These mistakes were corrected, while the system was
still under construction and before transforming it into an executable
version.
Integration testing will be performed by the knowledge engineer to
verify that all units are running together in the approved manner
(Aguilar et al. 2008; Ooshaksaraie et al. 2012). To run the system, the
user will provide the system with the required input data, such as the
type of area, description of the problem, its location, and the layer
where the problem is detected. The system is provided with many commands
that simplify the mission of the user regardless of his skills in
highway pavement construction and computer use. The user can enter his
input data by clicking the options presented by the system. The input
data are processed in the system to identify suitable solutions,
possible causes, preventive actions, and the effects of the problem if
no actions are taken to control it. The interaction between the user and
the ES-CCPRHP was tested by the knowledge engineer. Similar to unit
testing, integration testing was performed periodically during
construction of ES-CCPRHP to verify its capability to execute the
intended functions.
3.4.2. User satisfaction testing
A questionnaire survey was designed to test users'
satisfaction with ES-CCPRHP. Three groups of users were selected to
participate in these surveys. The first group includes 5 computer
specialists. The second group includes four experts in rigid highway
pavement construction, who were not involved in the knowledge
elicitation stage. The third group consists of 10 novice engineers. The
backgrounds of the users mentioned above are listed in Table 4.
The system was used by the participants to evaluate the system
depending on the questionnaire presented in Table 5. The result of
evaluation reflects the satisfaction of the users through their high
mean ratings (more than 3). Validation is performed to ensure that the
system represents the knowledge of the experts accurately (Aguilar et
al. 2008). The satisfaction of the users in the second group (domain
experts) can be considered as evidence for the system's validation
as these users are satisfied with the knowledge base content, its speed,
help facilities, system results, and other components in the system, as
listed in Table 5. Satisfaction of the users in all groups (computer
professionals, experts and novice engineers) indicates that the user
interface is friendly.
Questionnaire reliability was statistically tested by calculating
Cronbach's alpha, which was 0.969, indicating high reliability. The
mean and standard deviation values of the evaluation results for each
group of participants were calculated. The results were then
statistically tested by one-way ANOVA at a 95% confidence level (Table
5). The test shows no significant difference between the mean values of
the groups for questions 1, 2, 3, 4, 5, 10, and 16. However, a
significant difference was found between the mean values of the groups
for questions 6, 7, 8, 9, 11, 12, 13, 14, and 15 because the calculated
F value is higher than the tabulated F value (3.634). The test indicates
that at least one group differs from another, but does not show which
group pair or pairs cause the differences. Therefore, the least
significant difference (LSD) test by Tukey's method was adopted to
specify the pair or pairs from which the differences originate.
Following this method, we calculated the difference between each two
mean values and compared it with LSD. Table 6 abstracts the result of
the comparison. For all the questions, the differences between the mean
values of groups 1 and 2 and between groups 1 and 3 were higher than the
LSD, indicating significant differences between the mean values of group
1 and those of groups 2 and 3. Conversely, no significant differences
were found between the mean values of groups 2 and 3, except for
question 12. With respect to questions 6-9 and 11-15 (those related to
the evaluation of the knowledge included in the system), the mean values
in the second group (domain experts) were very high. This result
reflects the validity of the knowledge. The third group (novice highway
engineers) also exhibited high mean values, because they have a
reasonable background on the domain of the study. The first group had
mean values higher than 3 but with significant difference from groups 2
and 3 because this group was not familiar with pavement construction.
3.4.3. Behavior comparison between ES-CCPRHP and experts
Another measure for system validation involves presenting the
inputs and outputs of the system to the second set of experts. The
results given by the system are compared to the experts' opinions.
The system was found to be compatible with their opinions regarding
diagnosis, reasoning, conclusions and decisions. Moreover, the experts
approved the results presented by the system that related to the causes
of the problems, the preventive actions, and their possible effects. The
results of validation show that the system provides output that matches
the opinions of the second set of experts, as well as those of the first
set of experts, who were involved in the knowledge elicitation stage, on
whose knowledge the system was based. The overall compatibility between
the system and the first set of experts amounts to 97% as the knowledge
is already documented based on their expertise. The overall
compatibility between the system and the second set of experts amounts
to 86%. The variance between the system and the expert recommendation
can be justified by the additional information provided by the system
according to literature. The system is thus established to be valid and
can be used with confidence.
Table 7 presents the factors affecting the evaluation of plastic
concrete loads supplied to the construction site as an example of the
comparison process.
3.4.4. Testing by case study
This test was done to validate the system in a real environment. In
this step, a construction site was selected. At this site, the new
pavement was evaluated by ESCCPRHP, and the recommendations obtained
were compared with the recommendations reported by the Highway Division
in the Department of General Works. The results obtained from the system
were in line with the results of the reports. In addition, the results
given by the system were checked and approved by the second set of
domain experts.
In the selected section, 11 portions were reported as problematic
because of deficiencies in structural properties. For example, thickness
and concrete compressive strength were less than those required by the
design documents. Of these portions, we selected a final set of four
(shown in Table 8) as the illustrative examples. Input data were
obtained from the reports and entered into the system to evaluate the
specified portions. The design data were the same for all the portions,
whereas the data obtained from the site differed for each portion.
After running the system, the evaluation screen is displayed by
selecting the option "structural property deficiency problems"
from the main screen. The evaluation screen is shown in Figure 4.
The initial requested input data are:
[D.sub.req]: pavement slab depth required by the project documents
[design] (mm);
[D.sub.f]: constructed pavement slab depth measured in the field
(mm);
C[S.sub.req]: concrete compressive strength required by the project
documents [design] (MPa);
C[S.sub.f]: concrete compressive strength measured by testing the
cores drilled from the field (MPa).
The inference engine of the system will manipulate these inputs and
search the knowledge base for the matching rule as shown below:
IF [D.sub.req]--[D.sub.f] [less than or equal to] 13 mm and
C[S.sub.req] [less than or equal to] C[S.sub.f], THEN Accept the portion
IF [D.sub.req]--[D.sub.f] [less than or equal to] 13 mm and
C[S.sub.req] [greater than or equal to] C[S.sub.f], THEN Apply
Redesign Procedure
IF [D.sub.req]--[D.sub.f] [greater than or equal to] 13 mm and
C[S.sub.req] [less than or equal to] C[S.sub.f], THEN Apply
Redesign Procedure
IF [D.sub.req]--[D.sub.f] [greater than or equal to] 13 mm and
C[S.sub.req] [greater than or equal to] C[S.sub.f], THEN
Reject the portion.
When the system conclusion is "accept the portion", the
system will display an output screen to notify the user that the portion
can be accepted if there are no other problems; otherwise, the other
properties will be evaluated. In addition, the system offers to
calculate the value of price deduction, if required.
According to the second and third rules, the inference engine will
run the redesign subroutine to calculate the minimum thickness that is
structurally accepted on the basis of the AASHTO procedure. The
calculation is performed according to the following equation (AASHTO
1993):
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where: D--pavement slab depth calculated depending on field
measures; [W.sub.18]-predicted number of 80 KN (18,000 lb) equivalent
single axle loads (ESALs); [Z.sub.R]--standard normal deviation;
[S.sub.o]--combined standard error of traffic prediction and performance
prediction; [C.sub.d]--drainage coefficient; J-load transfer
coefficient; k--modulus of subgrade reaction; [DELTA]PSI--difference
between the initial design serviceability index and the design terminal
serviceability index = [p.sub.o] - [p.sub.t], where [p.sub.t] is the
terminal serviceability index and po denotes the initial design
serviceability index; [E.sub.c]--concrete elastic modulus = 57,000
[square root of C[S.sub.f]]; [S'.sub.c]--concrete modulus of
rupture = 7.5 [square root of C[S.sub.f]].
Redesign procedure:
1) Convert the units of C[S.sub.f] from MPa to psi;
2) Calculate [E.sub.c] and [S'.sub.c];
3) Calculate D;
4) Convert the unit of D from inch to mm;
5) Compare:
IF D--[D.sub.f] [less than or equal to] 13 mm, THEN Accept the
portion.
IF D--[D.sub.f] [greater than or equal to] 13 mm, THEN Reject the
portion.
Table 8 abstracts input, inference, and output for four portions in
this case study. Figures 4 and 5 illustrate the input and output
screens, respectively.
3.4.5. System updating and maintenance
The system will be updated when new construction technologies, such
as new materials, new concrete modifiers, new equipment, or new
techniques, are applied in the rigid highway pavement industry. The
system will also be updated when new design approaches are developed.
Moreover, the creativity of humans is boundless; therefore, the system
will be updated when new human expertise is developed. The operation of
the system for several days can cause problems; if so, the system can be
restarted to solve the problem.
[FIGURE 4 OMITTED]
Conclusion
Developing an expert system from scratch was the challenge of this
study. The evaluation and development stages of ES-CCPRHP are described
in detail in the domain of rigid highway pavement construction. The
developed system will be helpful for highway engineers to overcome
domain problems to detect problems and make decisions for solving
problems quickly. The need for the system was validated by a
questionnaire survey submitted to 30 highway engineers before the system
development began. The knowledge base of the system is based primarily
on human expertise and secondarily on a literature review. The knowledge
base includes the problems encountered in the domain, their causes,
preventive actions, instantaneous solutions, and their effects in a
classified form. A database of relevant knowledge is represented in the
form of rules and coded in this software coded in Microsoft Visual Basic
environment and supported by GIS, which is compatible with Visual Basic.
The system is verified and validated by extensive testing. Moreover the
system has a flexible and user friendly interface. The system has been
verified and validated and can be used confidently by end users. In
addition, it can be used as a database to archive the problems
encountered in the domain and to share highway engineers'
experiences and transfer expertise to successive generations of
engineers. Using this system as a foundation, other highway construction
expert system can be developed. Any knowledgeable engineer or any
competent user of Visual Basic can update this system under supervision
by a highway engineer and make it more resourceful for new engineers.
[FIGURE 5 OMITTED]
The system is unsuitable for problems that occur in embankment,
subgrade, subbase, and base construction, but it considers their effects
on the construction of concrete pavements. The system is also
inapplicable to prestressed concrete, pervious concrete,
roller-compacted concrete, self-consolidating concrete and asphaltic
shoulders. The system uses the AASHTO procedure in the redesign process
and does not include other procedures. The system works in Microsoft
Windows 2000 or higher and requires a memory space of 500 MB.
doi: 10.3846/13923730.2013.801905
Received 16 Jan 2012; accepted 2 May 2012
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Ahmed Mancy MOSA (a), Mohd Raihan TAHA (b), Amiruddin ISMAIL (a),
Riza Atiq O. K. RAHMAT (b)
(a) Sustainable Urban Transport Research Centre (SUTRA), Faculty of
Engineering and Built Environment, Kebangsaan University, 43600 UKM
Bangi, Selangor Darul Ehsan, Malaysia
(b) Civil and Structural Department, Faculty of Engineering and
Built Environment, Kebangsaan University 43600 UKM Bangi, Selangor Darul
Ehsan, Malaysia
Corresponding author: Ahmed Mancy Mosa
E-mail: iraq1214@yahoo.com
Ahmed Mancy MOSA. A PhD candidate at the Department of Civil and
Structural Engineering, Kebangsaan University, Malaysia. He was a
lecturer at the Department of Highway and Transportation, University of
Mustansiryah, Baghdad, Iraq. He received his Master's degree in
2000 from University of Mustansiryah, Baghdad, Iraq. His main research
interest is highway and transportation engineering, particularly on the
applications of expert system in pavement engineering.
Mohd Raihan TAHA. A Professor and a Head at the Department of Civil
and Structural Engineering, Kebangsaan University, Malaysia. He is a
Graduate member of The Institution of Engineers, Malaysia. He was the
recipient of various awards. He received his PhD in 1996 from Louisiana
State University, Baton Rouge, USA. His main research interest is
geotechnical and geo-environmental engineering, particularly on the
applications of nano-materials in soil remediation and improvement,
numerical modeling and constitutive relations.
Amiruddin ISMAIL. A Professor at the Department of Civil and
Structural Engineering, Kebangsaan University, Malaysia. He is a Head of
Sustainable Urban Transport Research Center (SUTRA), Faculty of
Engineering and Built Environment, Kebangsaan University, Malaysia. He
is a Professional Engineer, Board of Engineers, Malaysia, 1987. He was
the recipient of a number of various awards He received his PhD in 2002
from Kebangsaan University, Malaysia, Bangi. His main research interest
is highway and transportation engineering, particularly on the
applications of expert system in pavement engineering, traffic
engineering, public transport, and accidents' control.
Riza Atiq O. K. RAHMAT. A Professor at the Department of Civil and
Structural Engineering, University Kebangsaan, Malaysia. He is a Head of
Center of Academic advancement, University Kebangsaan, Malaysia. He is a
Professional Engineer at the Board of Engineers, Malaysia, 1987. He has
received a number of various awards. He received his PhD in 2002 from
Universiti Kebangsaan Malaysia, Bangi, Malaysia. His main research
interest is highway and transportation engineering, particularly on the
applications of expert system in pavement engineering, traffic
engineering, public transport, and accidents' control.
Table 1. Causes of cracking problems
Causes Cracking problems
i ii iii iv v vi vii viii
Hot weather (T x
> =
30[degrees]C,
low humidity,
windy)
Insufficient or x x x x
late curing
Improper x
finishing by
excessive
floating or
trowelling
Applying of x
cement powder
on surface
before
finishing
Restriction of x x x x
pavement slab
due to dowel
bars restraint
at joints or
due to
excessive
irregularities
of roadbed or
lack of
separation
membrane
High concrete x x
shrinkage due
to high water
content
High entrapped x
air content due
to improper
compaction
Late sawing or x x x
low depth of
contraction
joint grooves
High spaced x x
contraction
joints in
unreinforced
pavement
Presence of an x
active alkali
aggregate in
mixture
Improper x x
protection from
harmful weather
conditions
Trapping of x
stone pieces in
sealed grooves
Poorly x
compacted
concrete around
joint grooves
Discontinuity x
of the joint
groove in the
vertical
direction
Misaligned top x
and bottom
crack inducers
Omission of x
bottom cracks
inducers
Discontinuity x
of the joints
grooves at
their
intersections
Improper x
construction or
misalignment of
the joints
Table 2. Analysis of the questionnaire on the need for the
proposed system
No. Questions Group 1
Mean SD
Q1 The proposed system will be very important 4.800 0.632
Q2 The proposed system will help novice 4.800 0.632
engineers to solve the domain problems
Q3 The proposed system can help an engineer 5.000 0.000
to learn about the domain problems
Q4 The proposed system will be used as an 4.800 0.632
archive to document the domain problems
Q5 The proposed system can be used to 4.800 0.632
interchange the expertise among the
engineers in the domain of the study
Q6 The number of domain experts is not enough 3.800 1.033
to cover the volume of the projects
Q7 The number of domain experts is decreasing 3.000 1.333
No. Questions Group 2
Mean SD
Q1 The proposed system will be very important 4.800 0.615
Q2 The proposed system will help novice 4.900 0.447
engineers to solve the domain problems
Q3 The proposed system can help an engineer 4.500 0.889
to learn about the domain problems
Q4 The proposed system will be used as an 4.600 0.821
archive to document the domain problems
Q5 The proposed system can be used to 4.600 0.821
interchange the expertise among the
engineers in the domain of the study
Q6 The number of domain experts is not enough 4.300 0.979
to cover the volume of the projects
Q7 The number of domain experts is decreasing 3.800 1.361
No. Questions t p
Q1 The proposed system will be very important 0.000 1.000
Q2 The proposed system will help novice 0.502 0.619
engineers to solve the domain problems
Q3 The proposed system can help an engineer 1.763 0.089
to learn about the domain problems
Q4 The proposed system will be used as an 0.675 0.505
archive to document the domain problems
Q5 The proposed system can be used to 0.675 0.505
interchange the expertise among the
engineers in the domain of the study
Q6 The number of domain experts is not enough 1.295 0.206
to cover the volume of the projects
Q7 The number of domain experts is decreasing 1.528 0.138
Table 3. Experts involved in expertise elicitation
Expert Academic Years of Unstructured
number degree experience interviews
1 PhD 27 Yes
2 Master 24 Yes
3 Master 21 Yes
4 Master 20 Yes
Expert Structured Questionnaire Focus
number interviews interviews
(after
questionnaire
completion)
1 Yes Yes Yes
2 Yes Yes Yes
3 Yes Yes Yes
4 Yes No No
Table 4. Backgrounds of the users involved in system testing
Group User Specialization Degree Years of
experience
Group 1 1 Computer engineering Master 10
specialist
2 Information technology Master 7
specialist
3 Information technology Master 6
specialist
4 Software engineering Master 6
specialist
5 Computer science Master 5
specialist
Group 2 1 Domain expert Master 25
2 Domain expert Master 25
3 Domain expert Master 20
4 Domain expert Master 20
Group 3 1 Highway engineer Bachelor 2
2 Highway engineer Bachelor 2
3 Highway engineer Bachelor 2
4 Highway engineer Bachelor 2
5 Highway engineer Bachelor 2
6 Highway engineer Bachelor 1
7 Highway engineer Bachelor 1
8 Highway engineer Bachelor 1
9 Civil engineer Bachelor 0
10 Civil engineer Bachelor 0
Table 5. Result of ES-CCPRHP evaluation statistically tested by
ANOVA
No. Questions Group 1 Group 2
Mean SD Mean SD
Q1 ES-CCPRHP is easy to use 3.40 0.55 4.00 0.00
Q2 ES-CCPRHP runs quickly 5.00 0.00 5.00 0.00
Q3 The user interface is 4.00 0.00 4.00 0.82
user friendly
Q4 Obtaining an explanation 3.40 0.55 4.25 0.96
from ES-CCPRHP is easy
Q5 The explanations are useful 3.60 0.55 4.25 0.96
Q6 Help facilities are effective 3.40 0.55 4.25 0.96
Q7 The questions are helpful 3.40 0.55 4.50 0.58
Q8 The questions are clear 3.60 0.55 4.50 0.58
Q9 The terms are clear 3.60 0.55 4.50 0.58
Q10 Presentation of results is 3.60 0.55 4.25 0.50
clear
Q11 Presentation of results is 3.60 0.55 4.50 0.58
complete
Q12 ES-CCPRHP is helpful to 3.60 0.55 4.00 0.00
provide solutions
Q13 ES-CCPRHP is helpful to 3.60 0.55 4.80 0.63
specify the causes of
problems
Q14 ES-CCPRHP is helpful to 3.60 0.55 4.80 0.63
adopt preventive actions
Q15 ES-CCPRHP is helpful to 3.60 0.55 4.80 0.63
specify effects of problems
Q16 Generally, I am satisfied 3.80 0.44 4.00 0.82
with ES-CCPRHP
No. Questions Group 3
Mean SD F * p
Q1 ES-CCPRHP is easy to use 3.70 0.48 1.96 0.17
Q2 ES-CCPRHP runs quickly 5.00 0.00 -- --
Q3 The user interface is 4.20 0.79 0.20 0.82
user friendly
Q4 Obtaining an explanation 4.30 0.67 2.10 0.08
from ES-CCPRHP is easy
Q5 The explanations are useful 4.30 0.67 1.74 0.21
Q6 Help facilities are effective 4.40 0.52 4.29 ** 0.03
Q7 The questions are helpful 4.50 0.53 7.513 ** 0.005
Q8 The questions are clear 4.50 0.53 5.029 ** 0.020
Q9 The terms are clear 3.80 0.42 4.181 ** 0.035
Q10 Presentation of results is 4.10 0.57 1.884 0.184
clear
Q11 Presentation of results is 4.40 0.52 4.398 ** 0.030
complete
Q12 ES-CCPRHP is helpful to 4.60 0.52 7.812 ** 0.004
provide solutions
Q13 ES-CCPRHP is helpful to 4.90 0.45 11.336 ** 0.001
specify the causes of
problems
Q14 ES-CCPRHP is helpful to 4.90 0.45 11.336 ** 0.001
adopt preventive actions
Q15 ES-CCPRHP is helpful to 4.90 0.45 11.336 ** 0.001
specify effects of problems
Q16 Generally, I am satisfied 4.50 0.72 1.993 0.169
with ES-CCPRHP
Table 6. Least significant difference test
(Tukey's method)
Q F LSD Difference between
mean values
G1-G2 G1-G3 G2-G3
6 4.290 * 0.754 0.85 * 1 * 0.15
7 7.513 * 0.6493 1.1 * 1.1 * 0.0
8 5.029 * 0.6493 0.9 * 0.9 * 0.0
9 4.181 * 0.5814 0.9 * 0.2 * 0.7
11 4.398 * 0.6428 0.9 * 0.8 * 0.1
12 7.812 * 0.5688 0.4 * 1.0 * 0.6 *
13 11.336 * 0.6125 1.2 * 1.3 * 0.1
14 11.336 * 0.6125 1.2 * 1.3 * 0.1
15 11.336 * 0.6125 1.2 * 1.3 * 0.1
* There is significant difference between the
mean values in the group pair.
Table 7. Example of comparison between ES-CCPRHP and expert reasoning
Considered parameters First set of experts
in evaluation of
concrete properties
1 2 3
Overall concrete appearance
Homogenous/not homogenous [checked] [checked]
Coarse (harsh)/fine/regular [checked] [checked] [checked]
Stiff/high liquidity [checked] [checked]
Rich with cement/ [checked] [checked]
poor with cement
Segregated/not segregated [checked] [checked] [checked]
Contaminated/not [checked] [checked]
contaminated
Concrete temperature
Maximum [checked] [checked] [checked]
Minimum [checked] [checked] [checked]
Weather conditions
Ambient temperature [checked] [checked] [checked]
Rainy/windy/dusty/clear [checked] [checked] [checked]
Economical factors
money saving/money wastage [checked] [checked] [checked]
resources saving/ [checked] [checked]
resources wastage
Diagnosing criteria
Records reviewing [checked] [checked] [checked]
Testing and measurement [checked] [checked] [checked]
visual check [checked] [checked] [checked]
GIS
Conclusion
Effects on pavement: [checked] [checked] [checked]
no effects/minor
effects/severe effects
Solutions: accept/report/ [checked] [checked] [checked]
take additional samples/do
more tests/ rectify/reject
Causes [checked] [checked] [checked]
preventive actions [checked] [checked] [checked]
Considered parameters First set ES-CCPRHP Second set
in evaluation of of experts of experts
concrete properties
4 1
Overall concrete appearance
Homogenous/not homogenous [checked]
Coarse (harsh)/fine/regular [checked]
Stiff/high liquidity [checked] [checked]
Rich with cement/ [checked]
poor with cement
Segregated/not segregated [checked] [checked] [checked]
Contaminated/not [checked]
contaminated
Concrete temperature
Maximum [checked] [checked] [checked]
Minimum [checked] [checked]
Weather conditions
Ambient temperature [checked] [checked] [checked]
Rainy/windy/dusty/clear [checked] [checked] [checked]
Economical factors
money saving/money wastage [checked] [checked] [checked]
resources saving/ [checked] [checked] [checked]
resources wastage
Diagnosing criteria
Records reviewing [checked] [checked] [checked]
Testing and measurement [checked] [checked] [checked]
visual check [checked] [checked] [checked]
GIS [checked]
Conclusion
Effects on pavement: [checked] [checked] [checked]
no effects/minor
effects/severe effects
Solutions: accept/report/ [checked] [checked] [checked]
take additional samples/do
more tests/ rectify/reject
Causes [checked] [checked] [checked]
preventive actions [checked] [checked] [checked]
Considered parameters Second set of experts
in evaluation of
concrete properties
2 3 4
Overall concrete appearance
Homogenous/not homogenous [checked]
Coarse (harsh)/fine/regular [checked]
Stiff/high liquidity [checked] [checked]
Rich with cement/ [checked]
poor with cement
Segregated/not segregated [checked] [checked] [checked]
Contaminated/not
contaminated
Concrete temperature
Maximum [checked] [checked] [checked]
Minimum [checked] [checked]
Weather conditions
Ambient temperature [checked] [checked] [checked]
Rainy/windy/dusty/clear [checked] [checked] [checked]
Economical factors
money saving/money wastage [checked] [checked] [checked]
resources saving/ [checked]
resources wastage
Diagnosing criteria
Records reviewing [checked] [checked] [checked]
Testing and measurement [checked] [checked] [checked]
visual check [checked] [checked] [checked]
GIS
Conclusion
Effects on pavement: [checked] [checked] [checked]
no effects/minor
effects/severe effects
Solutions: accept/report/ [checked] [checked] [checked]
take additional samples/do
more tests/ rectify/reject
Causes [checked] [checked] [checked]
preventive actions [checked] [checked] [checked]
Table 8. Evaluation of structural properties in four portions
of the selected section in the case study
Design data: [W.sub.18] = 6000000; [D.sub.req] = 260 mm;
C[S.sub.req] = 35 MPa; J = 3.2; [C.sub.d] = 1; [p.sub.i] = 4.5,
[p.sub.t] = 3; [DELTA]PSI = 1.5; k = 54 MPa/m; R = 80%;
[S.sub.o] = 0.4; [Z.sub.R] = -0.841
N Input Reasoning in inference Output
engine
[D.sub.req] [D.sub.f] ES-CCPRHP
(mm) (MPa)
1 262 36 ([D.sub.req] = 260) - Accept
([D.sub.f] = 262) =
-2 < 13 and (C[S.sub.req]
= 35) < (C[S.sub.f] = 36)
THEN Accept the portion
2 255 30 ([D.sub.req] = 260) - Accept
([D.sub.f] = 255) = 5 <
13 and (C[S.sub.f] = 35) >
(C[S.sub.c] = 30) THEN
Apply Redesign Procedure:
C[S.sub.f] = 4350 psi,
[E.sub.c] = 3760000,
[S.sub.c] = 495, D = 10.4
in = 264 mm (D = 264) -
([D.sub.f] = 255) = 9 <
13 mm THEN Accept the
portion
3 249 27 ([D.sub.req] = 260) - Reject
([D.sub.f] = 249) = 11
< 13 and (C[S.sub.f] =
35) > (C[S.sub.c] = 27)
THEN Apply Redesign
Procedure: C[S.sub.f] =
3900, [E.sub.c] = 3560000,
[S.sub.c] = 468, [D.sub.f]
= 10.7 in = 272 mm (D =
272) - ([D.sub.f] = 249)
= 23 > 13 mm THEN Reject
the portion
4 245 32 ([D.sub.req] = 260) - Reject
([D.sub.f] = 245) =
15 > 13 and (C[S.sub.req]
= 35) > (C[S.sub.f] = 32)
THEN Reject the portion
N Output
Report Experts
1 2 3 4
1 Accept [cheked] [cheked] [cheked] [cheked]
2 Accept [cheked] [cheked] [cheked] [cheked]
3 Reject [cheked] [cheked] [cheked] [cheked]
4 Reject [cheked] [cheked] [cheked] [cheked]
