Prediction of Highway Bridge Performance by Artificial Neural Networks and Genetic Algorithms

Abstract

Bridge management systems (BMS) comprise various techniques need to help make decisions on the type of works that need to be performed to maintain the serviceability of a bridge and to extend its useful life. These decisions rely on current and future bridge conditions therefore it is essential for a BMS to accurately predict the future bridge performance, or in other words to assess the extent of bridge deterioration. Numerous deterioration models are reported in the literature. Most of these methods were developed using probabilistic approaches ranging from Markovian methods to regression techniques with various levels of detail. While offering mostly marginal improvements, such methods increase the complexity of the procedures and level of expertise needed. Besides, high reliance of these methods on historical data, which are likely to contain missing information, reduces the chances for a reliable model. The ability of learning in Artificial Intelligence (AI) methods provides promising results in modeling and forecasting even in the existence of non-linear complex relationships. Furthermore, easier use of AI tools provided by today's software makes AI methods even more attractive. In this study two AI tools, artificial neural networks (ANN) and genetic algorithms (GA), are utilized to develop models to predict bridge sufficiency ratings using current geometrical, age, traffic, and structural attributes as explanatory variables. Data is acquired from California Department of Transportation through the Internet and it includes 19120 structural bridge components owned and maintained by the State of California. The models developed by both ANN and GA provided promising and interpretable results. ANN models performed better when different models are constructed for different levels of sufficiency ratings. GA models outperformed ANN models while achieving a better goodness of fit even when using the whole data. However, remarkably prolonged training times for GA models might be considered as the only disadvantage for this type of application.

Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

“…Therefore, using the same input data from a different bridge network could produce very different results. Tokdemir et al [29] compared artificial neural networks and genetic algorithms in predicting bridge sufficiency ratings. This paper concluded that the genetic algorithms outperformed the artificial neural networks, however, the one disadvantage of the use of genetic algorithms is the lengthy training times required.…”

Markov Chain-Based Stochastic Modeling of Chloride Ion Transport in Concrete Bridges

“…A multilayer ANN is used to relate the condition of the bridge superstructure to the number of years of service of the bridge and other relevant inputs. Tokdemir et al (2000) use ANN to forecast the bridge condition as a function of bridge geometry, level of traffic, years in service, and structural attributes as explanatory variables.…”

Modeling Deterioration of Bridge Components with Binary Probit Techniques with Random Effects

“…The ANN technique was used to model bridge deterioration by relating age to the condition rating of the bridge superstructure (22). A detailed investigation was also carried out using ANN to predict bridge sufficiency ratings by incorporating age, geometry, and traffic attributes as explanatory variables (23). Although the ANN system is automated and utilizes a polynomial that best fits the data, it still has problems similar to the deterministic models.…”