**6. Conclusion**

Fuzzy logic inference methods can be used for managing bridges. Models based on FIS consider simultaneously several facts or knowledge combinations as rules and indicate the final answer or guess which is very close to practical existing situation as the hypothesis of the greatest belief. The reasoning process is very clear and easy to understand by users who are not experts in the performance of decision support systems. For bridge inspection no deteriorated area calculation is needed and the only requirement is the good inspector's judgment. It should be noted that fuzzy systems can tolerate some noise to predict the outputs. This means that during bridge deck inspection if in some cases judgment is not correct, but close to real condition, the proposed method can estimate the condition very well without a major difference from practical point of view. It is clear that in deterministic methods incorrect judgment or decision changes the category of the predefined condition and overall condition rating drastically. Another point that should be notified is that FIS can be applied in areas with high nonlinearity. When nonlinearity is high the prediction accuracy is expected to be improved by using ANFIS comparing to Mamdani's method. Accuracy of the method can be improved when an adaptive optimization method is used for constructing similar model based on the training data from inspections. FIS modeling is suitable for prioritization of repairing bridges and budgeting tasks in which relatively simple and practical reasoning is required for decision makers. Even in cases that human expertise is not available, we can still set up intuitively reasonable initial membership functions and start the learning process to generate a set of fuzzy if-then rules to approximate a desired data set. The efficiency of rule-based reasoning can be improved by comparing different inference methods. Generally the inferred results are in agreement with the expert's opinion, and can provide substantial assistance to authorities in their planning.

#### **7. References**


particle swarm optimization (PSO) algorithm working with discrete design variables is

In order to show the capabilities of the proposed methodology for identifying the multiple structural damages, two illustrative test examples are considered. The first example is a cantilever beam discussed in detail and the second one is a bending plate discussed in brief. The numerical results for these examples demonstrate that the combination of the ANFIS and PSO can produce an efficient tool for correctly detecting the locations and sizes of

Fuzzy logic inference methods can be used for managing bridges. Models based on FIS consider simultaneously several facts or knowledge combinations as rules and indicate the final answer or guess which is very close to practical existing situation as the hypothesis of the greatest belief. The reasoning process is very clear and easy to understand by users who are not experts in the performance of decision support systems. For bridge inspection no deteriorated area calculation is needed and the only requirement is the good inspector's judgment. It should be noted that fuzzy systems can tolerate some noise to predict the outputs. This means that during bridge deck inspection if in some cases judgment is not correct, but close to real condition, the proposed method can estimate the condition very well without a major difference from practical point of view. It is clear that in deterministic methods incorrect judgment or decision changes the category of the predefined condition and overall condition rating drastically. Another point that should be notified is that FIS can be applied in areas with high nonlinearity. When nonlinearity is high the prediction accuracy is expected to be improved by using ANFIS comparing to Mamdani's method. Accuracy of the method can be improved when an adaptive optimization method is used for constructing similar model based on the training data from inspections. FIS modeling is suitable for prioritization of repairing bridges and budgeting tasks in which relatively simple and practical reasoning is required for decision makers. Even in cases that human expertise is not available, we can still set up intuitively reasonable initial membership functions and start the learning process to generate a set of fuzzy if-then rules to approximate a desired data set. The efficiency of rule-based reasoning can be improved by comparing different inference methods. Generally the inferred results are in agreement with the expert's opinion, and can provide substantial assistance to authorities in their planning.

Aktan, A. E., Pervizpour, M., Catbas, N., Grimmelsman, K., Barrish, R., Curtis, J. & Qin, X.

Aydin, A. C., Tortum, A. & Yavuz, M. (2006). Prediction of concrete elastic modulus using

(2002). *Information technology research for health monitoring of bridge systems*, Drexel University Intelligent Infrastructure and Transportation Safety Institute,

adaptive neuro-fuzzy inference system. *Civil Engineering and Environmental Systems*, Vol. 23, No. 4, December 2006, pp. 295–309Baldwin, J. F. (1981). Fuzzy logic and fuzzy reasoning*. In E. H. Mamdani & B. R. Gaines (Eds.), Fuzzy reasoning and its* 

proposed to properly solve the damage problem.

damages induced (Fallahian & Seyedpoor, 2010).

**6. Conclusion** 

**7. References** 

Philadelphia, USA

*applications*, Academic Press, London

Chen, W. F. & Duan, L. (2000). *Bridge engineering handbook*. CRC Press


**22** 

*Iraq* 

**Neural Network and Adaptive Neuro-**

**Fuzzy Inference System Applied** 

*Thi-Qar University, College of Engineering, Civil Department* 

Soft computing is an approximate solution to a precisely formulated problem or more typically, an approximate solution to an imprecisely formulated problem (Zadeh, 1993). It is a new field appearing in the recent past to solve some problems such as decision-making, modeling and control problems. Soft computing is an emerging approach to computing which parallels the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision (Jang el at., 1997). It consists of many complementary tools such as artificial neural network (ANN), fuzzy logic (FL), and adaptive

Artificial neural network (ANN) model is a system of interconnected computational neurons arranged in an organized fashion to carry out an extensive computing to perform a mathematical mapping (Rafiq et al., 2001). The first interest in neural network (or parallel distributed processing) emerged after the introduction of simplified neurons by McCulloch & Pitts, (1943). These neurons were presented as models of biological neurons and as conceptual components for circuits that could perform computational works. ANN can be most adequately characterized as a computational model with particular properties such as the ability to adapt or learn, to generalize, or to cluster or organize data in which the

ANN has a large number of highly interconnected processing elements (nodes or units) that usually operate in parallel and are configured in regular architectures. The collective behavior of an ANN, like a human brain, demonstrates the ability to learn, recall, and generalize from training patterns or data. ANN is inspired by modeling networks of biological neurons in the brain. Hence, the processing elements in ANN are also called artificial neurons (Rafiq et al., 2001). Artificial neural network described in this chapter is mostly applied to solve many civil engineering applications such as structural analysis and design (Cladera & Mar, 2004a, 2004b; Hajela & Berke, 1991; Sanad & Saka, 2001), structural damage assessment (Feng & Bahng, 1999; Mukherjee et al., 1996), structural dynamics and control (Chen et al., 1995; Feng & Kim, 1998) and pavement condition-rating modeling

**1. Introduction** 

neuro-fuzzy inference system (ANFIS).

operation is based on parallel processing.

(Eldin & Senouuci, 1995).

**to Civil Engineering Problems** 

Mohammed A. Mashrei

