Thermal parameter identification of concrete dams based on hybrid particle swarm optimization using distributed optical fiber monitoring data

Abstract

The temperature of concrete arch dam during construction is affected by factors such as ambient temperature, cooling through water, and surface insulation. There is often a significant discrepancy between the actual thermal parameters and the values measured in indoor experimental tests. In this study, intelligent identification of thermal parameters is performed based on temperature monitoring data and an intelligence optimization algorithm so that the thermal characteristics of concrete in a real pouring environment can be obtained in real time. A hybrid algorithm of particle swarm optimization and grey wolf optimizer (HPSOGWO) is proposed. The performance of the hybrid particle swarm algorithm is verified to have some advantages over the other seven algorithms by twelve different test functions. Considering the influences of environmental temperature changes and multistage cooling water, HPSOGWO is utilized for inverse analysis of the thermal parameters of concretes with the same strength grade and concretes with different strength grades. The analysis results show that the concrete temperature values calculated based on the inversion of the HPSOGWO algorithm are in good agreement with the measured values, and the HPSOGWO algorithm has good adaptability in the inversion of thermal parameters of arch dams. The inversion results are of essential to clarify the relationship between temperature changes and thermal parameters.