Inferring rules for adverse load combinations to crack in concrete dam from monitoring data using adaptive neuro-fuzzy inference system

Abstract

The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature loads. As cracks especially unstable cracks are of great danger to the safety of dams, it is critical to avoid extremely adverse load combinations during the dam operations to achieve the stability of cracks. Conventionally, the adverse load combinations have to be determined empirically by experts based on specific dam site conditions. Therefore, it is attractive to apply quantitative instead of empirical methods to identify the adverse loading conditions. In this study, we employ an adaptive neuro-fuzzy inference system (ANFIS) to Chencun concrete dam. The ANFIS is able to help us build a relationship between the model inputs (reservoir water level and air temperature) and the model output (crack opening displacement). Based on this relationship, the rules of the adverse load combinations to the crack are generated directly from the monitoring data. The accuracy of the trained ANFIS is proved by comparing the modeling results and the monitoring data. Our work demonstrates that the ANFIS is a useful approach for accurately recognizing the rules of the adverse load combinations that can be used in the knowledge base of dam safety expert system. crack, concrete dam, load combination, adaptive neuro-fuzzy inference system, rules Citation:Xu H Z, Li X H. Inferring rules for adverse load combinations to crack in concrete dam from monitoring data using adaptive neuro-fuzzy inference system.

Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

“…On the contrary, Xu and Li [48] considered only 9 rules and could identify the worst environmental conditions for crack opening in Chencun Dam. ANFIS models can be as flexible and accurate as NN, while allowing for introducing engineering knowledge to some extent.…”

“…If the amount of rules and membership functions is low, the resultant model can be interpreted. Furthermore, an ANFIS model can be used for qualitatively describe dam behaviour, especially if the output is "fuzzyfied" into linguistic variables [48].…”

Spatio‐temporal data mining method for joint cracks in concrete dam based on association rules

“…Some artificial intelligence algorithms are also introduced into the modeling process of statistical models. For example, Xu and Li 11 employed an adaptive neuro‐fuzzy inference system to study the relationship between the reservoir water level, air temperature, and the COD. Dai et al 12 proposed a new methodology by combining the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals to identify the crack behavior of concrete dam.…”

Study on MPGA-BP of Gravity Dam Deformation Prediction

“…Dam cracks and displacement monitoring that reflect the structural aging and disease are widely used in various forms of dams (e.g., Xianghongdian Dam [1], Chencun Dam [2], and Dokan Dam [3]). Dam deformation is generally caused by three primary factors: temperature variation, chemical reactions, and live loads [4].…”