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- 1. Course: System Engineering for Water Resource – 박수완 교수Risk Assessment of Construction Projects UsingNetwork Based Adaptive Fuzzy System적응형 퍼지 시스템을 기반으로한 네트워크를 활용한건설 프로젝트의 위험성 평가Reviewed by:Hanifah Nebrian Sukma (하니파 네브리안 수크마) – 201183530Construction Management – Department of Civil & Environmental Engineering, PNU
- 2. Abstract – 추상 The main aim of this paper is determining the key risk factors of construction projects in Iran and developing an intelligent system to assess them. In this research, first the risks involved in construction projects has been identified and arranged in a systematic hierarchical structure. Questionnaire surveys and literature review were used for data collection. Next, based on the obtained data a network was based on the adaptive fuzzy system has been designed for the evaluation of project risks. The results show that the ANFIS models are more promising in the assessment of construction projects risks. The designed adaptive fuzzy system can learn from experience and past knowledge, can induce knowledge to future conditions by learning and updating itself and it can be applied to quantitative and qualitative factors. Risk Assessment of Construction Projects Using Network Based Adaptive Fuzzy System Mehdi Ebrat, Reza Ghodsi Department of Industrial and Mechanical Engineering, Qazvin Islamic Azad University, Iran Department of Industrial Engineering, College of Engineering, University of Tehran, Iran2 / 28
- 3. Review – 리뷰 Risk Assessment (위험성 평가) A scientific and mathematical probability of the possible risk in certain activities. A „what could happen?‟ scenario using statistics from many different sources including animal behavior research, prior statistics, environmental issues including weather patterns and fault lines, along with many other sources to assess a scientific and mathematical value of the possibility that a person may be injured or killed performing certain activities. An integral part of the risk management plan, studying the probability, the impact, and the effect of every known risk on the project, as well as the corrective action to take should that risk occur.3 / 28 http://bit.ly/t5hMtE , http://bit.ly/v4YEkA
- 4. Introduction – 소개 Construction Risk (건설 위험) Construction projects have numerous risks due to different factors such as weather changes, cultural differences of involved people, instability in politics, possibility of governmental policies changes, and financial and economical issues. The nature of construction projects accompanies with imposed uncertainties and depends on the person‟s thinking prototype in the process of risk analysis. Many methods used to represent the risk to form a model for qualitative risk evaluation of construction projects. Yet Artificial Intelligence (AI) techniques can have broad applications in risk management.4 / 28 Ebrat, Ghodsi
- 5. Introduction – 소개 Neural Network & Fuzzy Model Neural networks and fuzzy modeling are two systemic patterns which can be used in construction management well. Neural networks can be viewed as the black box in processes which are not known but there are many observations and data regarding to this. Fuzzy models can be viewed as white box in which human knowledge can be used for modeling the system with no need to data.5 / 28 Ebrat, Ghodsi
- 6. Introduction – 소개 ANFIS (Adaptive Neuro-Fuzzy Inference System) A new technique called ANFIS (Adaptive Neuro-Fuzzy Inference System) can combine the concepts of neural network with fuzzy inference systems to create an efficient method which is known nowadays as soft computing. Three main features of comparative systems than to other methods of risk evaluation are as follows: Monitoring the organization in order to finding of risk Flexibility in reaction to risk Comparative Learning ability of organization resources in reaction to risk6 / 28 Ebrat, Ghodsi
- 7. Risk Factors Identification – 위험 요인 식별 Risk FactorsBreak-down Structure Identification Literature review Field survey Identified the following risks by propose a break- down structure 7 / 28 Ebrat, Ghodsi
- 8. Data Collection – 데이터 수집 Data Collection Risk matrix In collection of initial data, the questionnaire was used in which experts stated their opinions as the probability of occurrence of risks and the severity of risk in the form of 5- choice scale of (very unlikely, unlikely, even, likely, very likely) and (very low, low, medium, high, Catastrophic), respectively. For obtaining the value of risk for each factor (in terms of a linguistic variable), we use the table of fuzzy values originated from PMBOK / Project Management Body of Knowledge (ver. 2004).8 / 28 Ebrat, Ghodsi
- 9. Data Collection – 데이터 수집 Data Collection By use of this technique, data of probability of occurrence, severity, and risk of each factor are gathered in form of linguistics data. In the next step, these data should be converted to fuzzy data. In conversion of linguistic data to fuzzy data, triangular fuzzy numbers have been used. After converting the linguistic variables into triangular fuzzy numbers, the center of area (COA) method (Zhao and Govind, 1991) was performed for defuzzifying the triangular fuzzy numbers into corresponding best non-fuzzy performance (BNP) values. In the BNP method, if A = ( al , am , au ) shows a triangular fuzzy number, its deterministic value is calculated from Equation (1).9 / 28 Ebrat, Ghodsi
- 10. Data Collection – 데이터 수집 Linguistic Values and Fuzzy Values Of The Probability, Severity, and Risk Risk matrix10 / 28 Ebrat, Ghodsi
- 11. Data Collection – 데이터 수집 Proposed Fuzzy System for Risk Assessment The goal of quantitative risk analysis is the numerical analysis of probability of occurrence of each risk and its outcomes on project objectives. Based on this definition, the value of each risk is a function of probability of occurrence of risk and severity and effect of that on project objectives.11 / 28 Ebrat, Ghodsi
- 12. Data Collection – 데이터 수집 Proposed Fuzzy System for Risk Assessment In neuro-fuzzy systems, for determining the relation between inputs and outputs, there are 2 algorithms: hybrid learning and error back-propagation. This linear relation is in 2 forms of first order and zero order. In the second form, this linear relation becomes a constant number. Finally, outputs of each of rules are summed in the weighted manner. In this system, 2 inputs are used. The linear relation between these inputs would be:12 / 28 Ebrat, Ghodsi
- 13. Data Collection – 데이터 수집 Neuro-fuzzy System with 2 Inputs and 1 Output13 / 28 Ebrat, Ghodsi
- 14. Data Collection – 데이터 수집 Neuro-fuzzy System with 2 Inputs and 1 Output This system formed by 5 layers which is shown in diagram on previous page. Here, x and y are numerical inputs while A and B are numerical variables. These variables are identified by membership functions. Also, p, q, and r are parameters that determine the relation between input and output. The1st layer indicates how much each numerical input belongs to different fuzzy set. The output of the first layer is calculated by equation (3). Where and are the membership functions for fuzzy sets of A and B.14 / 28 Ebrat, Ghodsi
- 15. Data Collection – 데이터 수집 Neuro-fuzzy System with 2 Inputs and 1 Output In the 2nd layer, operators” AND“ and “OR“ are used for achieving the output. This value determined how much a special rule is true in different values of inputs. The output of this layer is obtained by multiplying of the earlier results, can be calculated by equation (4). In the 3rd layer, each of the outputs of the previous layer is divided to all of outputs of that rule (equation (5)).15 / 28 Ebrat, Ghodsi
- 16. Data Collection – 데이터 수집 Neuro-fuzzy System with 2 Inputs and 1 Output In the 4th layer, involvement of each rule for calculation of model output is computed by equation (6). The 5th layer is named as output layer. In this layer, the outputs of the previous neurons are summed with each other and finally, by defuzzification, fuzzy outputs are converted to numerical outputs. Equation (7) shows how this conversion is done.16 / 28 Ebrat, Ghodsi
- 17. Data Collection – 데이터 수집 Neuro-fuzzy System with 2 Inputs and 1 Output In this state, the network learns the rules based on the supervised learning rules. Now, the neural network should be trained that each of values of a, b, c, p, q, r changes and finally, the best values for parameter are obtained. With assumption a, b, and c are constant; equation (8) would be, Here, the least square method is used to obtain the best parameters. If the membership functions of inputs are unknown as well, the solution space would be very large and convergence will take more time. This needs a forward step and backward step. In the forward step, errors are calculated and in the backward step, operations are done on the parameters.17 / 28 Ebrat, Ghodsi
- 18. Performance Evaluation – 성과 평가 Performance Evaluation In the previous sections, we explained how to create different units of fuzzy inference system for risk assessment of construction project in great details. In this section, we explain how risk is going to be assessed and how performance evaluation of system is designed. To do so, as an example, we do evaluate the performance of designed system for risk in technical skill and knowledge. The existed data on technical skill and knowledge in the data base of this system are used for train of neural network which sets the systems parameters.18 / 28 Ebrat, Ghodsi
- 19. Performance Evaluation – 성과 평가 Performance Evaluation At , Ft , N represent real data, predicted data, and the number of data, respectively: Different performed scenarios with their errors have been presented in the next table.19 / 28 Ebrat, Ghodsi
- 20. Performance Evaluation – 성과 평가Performance Evaluation 20 / 28 Ebrat, Ghodsi
- 21. Performance Evaluation – 성과 평가 Probability and Severity Membership Functions From the table we can see triangular membership function (trimf) is the best one because it has the minimum errors of MAPE and RMSE and the maximum correlation coefficient. Membership functions of probability of occurrence and risk severity have been shown in following diagram:21 / 28 Ebrat, Ghodsi
- 22. Performance Evaluation – 성과 평가 Performance Evaluation Plot of train error against epochs Plot of test error against epochs * epoch = time22 / 28 Ebrat, Ghodsi
- 23. Performance Evaluation – 성과 평가 Performance Evaluation As can be observed, train error increases with an unstable trend increase. However, with increase of the number of epochs, error reduces and error fluctuations reach to steady state. Error values of MAPE and RMSE for training are 2.8947x10^-6 and 1.5489x10^-8 at the end of epoch 500. These low values indicate that the network works well. Also, the low value of test error indicates the reliability of system for risk assessment. Correlation between test data and data predicted by ANFIS is 0.9185. Closeness of this value to 1 indicates the fitness of designed system for risk evaluation.23 / 28 Ebrat, Ghodsi
- 24. Performance Evaluation – 성과 평가 Performance Evaluation Diagram 10 presents a 3-D diagram (surface) of rules in the fuzzy system. As mentioned, these rules have been developed based on Sugeno. In addition, as can be seen in the ANFIS structure of Diagram 7, the logic operator used in combining the inputs states is AND.24 / 28 Ebrat, Ghodsi
- 25. Review – 리뷰 Sugeno-Type Fuzzy Inference The fuzzy inference process discussed so far is Mamdanis fuzzy inference method, the most common methodology. Sugeno or Takagi-Sugeno-Kang method of fuzzy inference introduced in 1985, is similar to the Mamdani method in many respects. The first two parts of the fuzzy inference process, fuzzifying the inputs and applying the fuzzy operator, are exactly the same. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant. Because it is a more compact and computationally efficient representation than a Mamdani system, the Sugeno system lends itself to the use of adaptive techniques for constructing fuzzy models. These adaptive techniques can be used to customize the membership functions so that the fuzzy system best models the data.25 / 28 http://bit.ly/sCd4Ey
- 26. Comparison with Mamdani – 비교 Sugeno vs Mamdani Advantages of the Sugeno Method: It is computationally efficient. It works well with linear techniques (e.g., PID control). It works well with optimization and adaptive techniques. It has guaranteed continuity of the output surface. It is well suited to mathematical analysis. Advantages of the Mamdani Method: It is intuitive. It has widespread acceptance. It is well suited to human input.26 / 28 http://bit.ly/sCd4Ey
- 27. Conclusion – 결론 Conclusion Most of traditional methods in risk assessment are based on statistical or computing techniques. Many of these methods cannot cover data related to quality factors which have high effect on risk evaluation. Thus, because of high abilities of artificial neural network in the prediction, learning, and modeling of human knowledge, ANFIS was applied based on experts‟ opinions for risk assessment of construction projects. Briefly, advantages of ANFIS in prediction and evaluation of risks can be stated as follows: can learn from experience and past knowledge. can induce knowledge to future conditions by learning and updating itself. can be applied to quantitative and qualitative factors.27 / 28 Ebrat, Ghodsi
- 28. Presented by Hanifah Nebrian Sukma on Thursday, December 15th 2011Risk Assessment of Construction Projects UsingNetwork Based Adaptive Fuzzy System적응형 퍼지 시스템을 기반으로한 네트워크를 활용한건설 프로젝트의 위험성 평가 고맙습니다

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