Year: 2025
Type: article
Abstract: The current evaluation models for earth-rock dam compaction quality seldom incorporate parameter uncertainty considerations. Additionally, the existing models frequently demonstrate constrained prediction accuracy and generalization capabilities. To resolve these issues, we present an intelligent evaluation method for the compaction quality of earth-rock dams that explicitly accounts for parameter uncertainty. The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm’s Ntree and Mtry parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. This model aims to reveal the complex nonlinear mapping relationship between input influencing factors, such as compaction parameters, material source parameters, and meteorological factors, and the compaction quality. The proposed model improves the prediction accuracy, generalization ability, and robustness. The improved firefly optimization-based random forest (IFA-RF) is applied in practical engineering projects, and the results validate that this method can reliably and accurately predict the compaction quality of earth-rock dam construction in real time (R = 0.90107, MSE = 0.0000602, p = 0.000) and thereby guide remedial measures to ensure engineering safety and quality compliance.
Source: Applied Sciences
DOI: 10.3390/app15074024
Authors: Weiwei Lin, Yuling Yan, Pu Xu, Xiao Zhang, Yichuan Zhong
Institution: Fuzhou University
Language: English
Topic: Dam Engineering and Safety
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G1208853466, G1431380416, G7832333152
OpenAlex ID: W4409219944