Inversion analysis of thermodynamic parameters of concrete dam based on intelligent algorithm
Year: 2025
Type: conference-paper
Abstract: Against the backdrop of rapid advancements in intelligent algorithms and deep learning, this study aims to propose a rational thermal control analysis strategy and parameter inversion method tailored for concrete gravity dams. Starting from the theoretical foundations and engineering practices of thermal control and crack prevention in concrete gravity dams, the study integrates traditional back analysis, parameter inversion techniques, and intelligent algorithms. It compares their fundamental principles, advantages, and limitations to introduce current approaches for thermodynamic parameter inversion and the latest progress in smart dam research. Findings indicate that traditional parameter inversion methods suffer from significant limitations, including poor adaptability to complex working conditions, non-uniqueness of solutions, and high computational costs. In contrast, intelligent algorithms demonstrate great potential for thermophysical parameter inversion, offering a promising new paradigm to overcome these bottlenecks and providing technical support for smart dam systems. Based on the above results, future research must focus on deeply integrating machine learning with traditional methods to develop more economical and efficient approaches for thermodynamic parameter inversion and to advance intelligent dam systems.
Authors: Yue Yue, Shifa Xia, Qiaorong Sun
Language: English
Topic: Dam Engineering and Safety
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7127563379