In construction projects, construction errors affect negatively to the production, that influences the overall of the project in both time and budget. Generally, construction companies could not estimate this kind of errors during the bidding process. In this case, these companies did not consider important issues on the budget of the contract, and in the contracting period, project participants assumed that the project would be executed as it scheduled and designed. During the project, different construction processes’ costs are higher than estimated values due to construction errors. The errors that were recognized during the construction process cause time and financial losses, on the other hand, the errors that were noticed after the project’s termination cause repair and correction costs. Moreover, the company may gain a bad reputation in the sector. The key points of this study are to analyze project costs by considering construction errors and re-construction costs due to labor errors by using fuzzy interpretation mechanism. This methodology is applied to a residential construction project. With using of this methodology, forthcoming extra costs related to construction errors can be estimated. And some precautions can be taken for further legal conflicts between parties.

1. DETERMINING COSTS OF
CONSTRUCTION ERRORS, BASED ON
FUZZY LOGIC SYSTEMS
Presentation Date : 16/11/2018
Authors:
M.Sc. Civil Eng. M. Lemar ZALMAI
Assist. Prof. Dr. Cemil AKÇAY
Prof. Dr. Ekrem MANİSALI
1

2. Contents
 Introduction
 Research Question
 Construction Project Life Cycle
 Fuzzy Expert Systems
 Case Study
 Discussion and Conclusion
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M. L. Zalmai, C. Akçay, E. Manisalı

3. Introduction
3
In construction projects, construction errors affect negatively to the
production, that influences the overall of the project in both time and budget.
Generally, construction companies could not estimate this kind of errors
during the bidding process. In this case, these companies did not consider
important issues on the budget of the contract, and in the contracting period,
project participants assumed that the project would be executed as it
scheduled and designed. During the project, different construction processes’
costs are higher than estimated values due to construction errors.
M. L. Zalmai, C. Akçay, E. Manisalı

4. 4
The errors that were recognized during the construction process cause
time and financial losses, on the other hand, the errors that were
noticed after the project’s termination cause repair and correction costs.
Moreover, the company may gain a bad reputation in the sector.
The key points of this study are to analyze project costs by considering
construction errors and re-construction costs due to labor errors by
using fuzzy interpretation mechanism. This methodology is applied to a
residential construction project. With using of this methodology,
forthcoming extra costs related to construction errors can be
estimated. And some precautions can be taken for further legal
conflicts between parties.
M. L. Zalmai, C. Akçay, E. Manisalı

5. Construction projects' life cycle
5Figure 1: Representative construction project life cycle (PBOK Guide, 2004)

6. Construction projects' life cycle
6
The Construction projects' life cycle is shown in the figure as above; this
process starts with the feasibility and will be continuing with designing
and planning. For the construction firms, most of the errors are
happened in the production process and the main construction's contract
affected in both time and financial issues.
The intent of this study is to remove the error from the estimated budget
and finding the most important factors to achieve the project in expected
duration as well as control the technical staff during the project activities.
M. L. Zalmai, C. Akçay, E. Manisalı

7. Fuzzy Expert Systems
7
A fuzzy expert system is comprised of fuzzy membership functions
and rules. It contains four main parts: fuzzification, inference,
composition, and defuzzification
Figure 2: Architecture of a fuzzy expert system
M. L. Zalmai, C. Akçay, E. Manisalı

8. Fuzzy Expert Systems
8
The fuzzification process transforms the crisp input into a fuzzy input
set. The inference process uses the fuzzy input set to determine the
fuzzy output set using rules formulated in the knowledge base and the
membership functions.
The composition process aggregates all output fuzzy sets into a single
fuzzy set. Finally, the defuzzification process calculates a crisp output
using the fuzzy set produced by the composition process.
M. L. Zalmai, C. Akçay, E. Manisalı

9. Case Study
9
1. Fuzzification
a. Project Duration
The average standard duration of the residential construction projects is 18 months.
The completion of the project period less than 10 months and more than 28 months
will be out of assessment, it will be related to the abnormal condition. Among this
interval, duration has obscured and defined in the table below:
M. L. Zalmai, C. Akçay, E. Manisalı

10. 10
b. Technical Staff Ratio:
The numbers of manufacturing controller staff should be converted to a
common control ratio because the numbers of manufacturing controller staff
are different according to the project's size. The Controller staff per
construction m2 will be a correct approach. In a housing project with a
total construction area of 50,000 m2, 20 technical staff is considered within
normal standards. With this approach, the manufacturing controller technical
staffs per construction m2 data are illustrating as follows:

11. 11
c. Error Cost Ratio:
The error cost ratio is the construction direct cost or the construction
incorrect cost. This ratio is reflected according to the company's
experiences as follows;
M. L. Zalmai, C. Akçay, E. Manisalı

12. 12
2. Rule Case
The base on experiences of previous projects, tow rules were created
in the following manners:
a. If the project duration is short and technical staff ratio is low, then
the error cost is high.
b. If the project duration is short and technical staff ratio is normal,
then the error cost is normal.
M. L. Zalmai, C. Akçay, E. Manisalı

13. 13
3. Fuzzy Interpretation
In this study, under the rules described above, the fuzzy interpretation
of a project with a project duration of 17 months and a technical staff
ratio of 3.6x10-4 will be done.
Integration process according to Mamdani interpretation;

14. 14
4. Defuzzification
There are different types of defuzzifiers. The two popular techniques used for
defuzzification process are called Mamdani inference method and Center of
Gravity method modeling. (Yeng, 2000). In this study, the Center of Gravity
method modeling has been used.
The final step in this study, the defuzzification process of results which was
obtained according to the fuzzy rule base that created before. the following
method was preferred due to the symmetrical output values. With this method
has been found as follows;

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These results found the indicates that the error costs rate of a project
which the construction company wants to show the realization in the 17
months with 3.6x10-4 technical staff ratio, the project direct cost will be
0.75 percent. According to the past data and defined the rules, for a
project with a direct cost of 100.000.000 TL. the error cost will be
750.000 TL.
Results:
M. L. Zalmai, C. Akçay, E. Manisalı

16. Conclusions
In this study, a fuzzy rule-based model was established based on a
construction company's experience and the company’s previous project
data including error cost rates, project durations and technical staff
ratio over total employee number in each project. The error cost of the
as-planned project is calculated by using the Mamdani Fuzzy
interpretation mechanism with this rule base and data.
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M. L. Zalmai, C. Akçay, E. Manisalı

17. Conclusions
Also, besides the possibility of estimating error costs by using technical
staff rates before the project, contractors can organize their technical
staff number to adjust error cost rates and project durations.
In this study, a generic methodology is proposed that different kind of
risk factors such as occupational health safety, accident rates, and
budget variance rates can be estimated by using previous data and
expert opinion.
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M. L. Zalmai, C. Akçay, E. Manisalı

18. References
 A. Schwartz, H. Do. (2013). A fuzzy expert system for cost-effective regression testing strategies.
Proceedings of the 29th IEEE International Conference on Sofware Maintenance (ICSM), September, pp. 1-10.
 E. H. Mamdani, S. Assilian. (1975). An experiment in linguistic synthesis with a fuzzy logic controller,
Int. J. Man-Mach. Stud. 1-13.
 Idri, A. and Abran, A. (2001). Towards a fuzzy logic based measures for software projects similarity. 7th
International Symposium on Software Metrics, pp. 85-96, England, UK.
 Simon, D. (2003). Introduction to fuzzy control. Courtesy of Embedded Systems Programming.
http://www.embedded.com/showArticle.jhtml?articleID=10700619.
 Sivanandam, S., Sumathi, S. and Deepa, S. (2007). Introduction to Fuzzy Logic using MATLAB.
Springer-Verlag Berlin Heidelberg.
 Ying, h, (2000). Fuzzy Control and Modeling analytical Foundations and Applications. IEEE Press.
 Z. Xu, K. Gao, T.M. Khoshgoftaar. (2005). Application of fuzzy expert system in test case selection for
system regression test. Proceedings of IEEE International Conference on Information Reuse and Integration, August,
pp. 120-125.
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M. L. Zalmai, C. Akçay, E. Manisalı

19. THANK YOU FOR
YOUR PATIENCE
19
MSc. Mohammad Lemar ZALMAI (lemar_zalmai07@hotmail.com)
Yrd. Doç. Dr. Cemil AKÇAY (cakcay@istanbul.edu.tr)
Prof. Dr. Ekrem MANİSALI (ekmanisa@istanbul.edu.tr)
DETERMINING COSTS OF
CONSTRUCTION ERRORS, BASED ON
FUZZY LOGIC SYSTEMS