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In many civil engineering projects, Piled Raft Foundations (PRFs) are usually preferred where the incoming load from the superstructures is very high. In geotechnical engineering practice, the settlement of soil layers is a critical issue for the serviceability of the structures. Thus, assessment of risk associated with the structures corresponding to the maximum allowable settlement of soils needs to be carried out in the design phase. In this study, reliability analysis of PRF based on settlement criteria is performed using a high-performance hybrid soft computing model. The new approach is an integration of the artificial neural network (ANN) and a recently developed meta-heuristic algorithm called equilibrium optimizer (EO). The concept of reliability index was used to explore the feasibility of a newly constructed hybrid model of ANN and EO ( i.e ., ANN-EO) against the conventional approach of calculating the probability of failure of PRF. Experimental results show that the proposed ANN-EO attained the most accurate prediction with R 2  = 0.9914 and RMSE = 0.0518 in the testing phase, which are significantly better than those obtained from conventional ANN, multivariate adaptive regression splines, and genetic programming, including the ANN optimized with particle swarm optimization developed in this study. Based on the experimental results of different settlement values, the newly constructed ANN-EO is very potential to analyze the risk associated with civil engineering structures. Also, the present study would significantly contribute to the knowledge pool of reliability studies related to piled raft systems because the works of literature on reliability analysis of piled raft systems are relatively scarce.

Reliability Analysis of Piled Raft Foundation Using a Novel Hybrid Approach of ANN and Equilibrium Optimizer