Performance Evaluation System for Main Reinforced Concrete Girders of Existing Bridges

Abstract

A novel performance evaluation system for main reinforced concrete girders of existing bridges evaluates durability and load-carrying capability by using technical specifications and results of simple visual inspections. Three features of the system are described. The first feature is the application of an expert system. Because the purpose of this study is to develop a novel system to evaluate the performance of a bridge element with limited information provided from simple visual inspections, it is necessary to embed into the system the knowledge of bridge experts. The second feature is a neuro-fuzzy hybrid inference. The system performs a fuzzy inference to simulate the inference mechanism used by experts for evaluating bridges. This process is integrated into a computer as a neural network; therefore, the system improves the knowledge base by using the back-propagation method. The third feature is the visualization of the inference process. The system must clearly explain how the outputs are calculated because the road networks, including bridges, represent important investments that must be carefully monitored. The learning capability of the system is demonstrated on an existing bridge in Japan. The effectiveness is verified by comparing the diagnostic results provided by a bridge expert and those proposed by the system.

Get full access to this article

View all access options for this article.

References

Office of Chuugoku, Ministry of Land, Infrastructure, and Transport, Government of Japan. Chuugoku Technical News [in Japanese], Vol. 121, Jan. 2002, pp. 4–5.

Miyamoto

, Kawamura

, and Nakamura

. Bridge Management System and Maintenance Optimization for Existing Bridges. Computer-Aided Civil and Infrastructure Engineering, Vol. 15, 2000, pp. 45–55.

Miyamoto

, Kawamura

, and Nakamura

. Practical Applications of a Bridge Management System in Japan. In Transportation Research Record: Journal of the Transportation Research Board, No. 1696, TRB, National Research Council, Washington, D.C., 2000, pp. 14–25.

Miyamoto

, Kawamura

, and Nakamura

. Development of a Bridge Management System for Existing Bridges. Advances in Engineering Software, Vol. 32, Nos. 10-11, Oct. 2001, pp. 821–833.

Kawamura

, Miyamoto

, Frangopol

D. M.

, and Kimura

. Performance Evaluation of Concrete Slabs of Existing Bridges Using Neural Networks. Engineering Structures, Vol. 25, No. 12, Oct. 2003, pp. 1455–1477.

Frangopol

D. M.

, and Kong

J. S.

. Cost-Effectiveness of Bridge Maintenance Strategies. In Maintaining the Safety of Deteriorating Civil Infrastructures ( Miyamoto

and Frangopol

D. M.

, eds.), Practical Maintenance Engineering Institute of Yamaguchi University, Japan, 2003, pp. 55–70.

Frangopol

D. M.

, and Kong

J. S.

. Expected Maintenance Cost of Deteriorating Civil Infrastructures. In Life-Cycle Cost Analysis and Design of Civil Infrastructure Systems ( Frangopol

D. M.

an Furuta

d H.

, eds.), American Society of Civil Engineers, Reston, Va., 2000, pp. 22–47.

Mitchell

T. M.

Machine Learning. McGraw-Hill Series in Computer Science. McGraw-Hill, New York, 1997.

Pedrycz

Computational Intelligence: An Introduction. CRC Press, Boca Raton, Fla., 1998.