| 林威伟,张君,王晓玲,王佳俊,余红玲.基于改进证据理论的土石坝碾压质量综合评价方法[J].水利学报,2025,56(1):117-129 |
| 基于改进证据理论的土石坝碾压质量综合评价方法 |
| A comprehensive evaluation method for rolling quality of earth-rock dam based on improved evidence theory |
| 投稿时间：2024-01-15 |
| DOI：10.13243/j.cnki.slxb.20240022 |
| 中文关键词: 土石坝施工 碾压质量评价 不确定性 ACGWO-RF算法 改进证据理论 |
| 英文关键词: earth-rock dam construction rolling quality evaluation uncertainty ACGWO-RF algorithm improved evidence theory |
| 基金项目:国家自然科学基金青年项目(52309165);国家自然科学基金重大项目课题(52494973) |
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| 中文摘要: |
| 碾压质量的好坏对于大坝沉降和变形是否满足要求有极大的影响。目前碾压质量评价研究采用随机选择有限点进行试坑试验获取数据评价全仓面碾压质量，导致评价参数和预测结果存在随机、模糊、灰色和未确知性。针对上述问题，本文提出基于ACGWO-RF算法和证据理论的碾压质量评价及馈控方法。首先，采用自适应因子和混沌理论提高GWO算法的搜索能力，并基于ACGWO优化适用于小样本数据集的RF算法的Ntree和Mtry参数，进而建立基于ACGWO-RF算法的碾压质量预测模型，以揭示碾压参数、料源参数和气象参数等输入影响参数与碾压质量的复杂非线性映射关系;进一步，针对碾压质量评价中存在的随机、模糊、灰色和未确知性以及单一评价指标存在的片面性和准确性欠佳问题，提出耦合全仓面连续监测指标(CV值)和评价模型预测结果(干密度、压实度)的碾压质量综合评价方法，采用模糊隶属度对三者的合格性进行模糊评价，并采用能够处理多种不确定性的证据理论进行证据融合;最后，提出多级反馈控制机制对现场碾压作业进行反馈控制。工程实例表明，与现有评价算法相比，所提方法具有高精度(R=0.839)、强泛化能力(R=0.793)以及强鲁棒性。基于证据理论的综合评价方法能够考虑有限随机试坑数据评价碾压质量存在的不确定性，同时将CV值对噪声敏感性降低69.8%，且多级反馈控制机制能够有效保障现场碾压质量。 |
| 英文摘要: |
| The quality of compaction during rolling significantly influences whether dam settlement and deformation meet requirements.Current research on evaluation models relies on limited random test pit data，leading to issues of randomness，fuzziness，grey areas，and uncertainty.To address these challenges，this study proposes a rolling quality evaluation and feed control method based on the ACGWO-RF algorithm and evidence theory.Initially，adaptive factor and chaos theory enhance the search capability of the GWO algorithm，while the Ntree and Mtry parameters of the RF algorithm，suitable for small sample datasets，are optimized based on the proposed ACGWO.Subsequently，a rolling quality prediction model employing the ACGWO-RF algorithm is established to elucidate the intricate nonlinear mapping relationship between rolling quality and influencing variables such as rolling parameters，material source parameters，and meteorological parameters.Moreover,to address challenges in rolling quality evaluation related to randomness,fuzziness,grey areas,and uncertainty,as well as mitigating the one-sidedness and poor accuracy of a single evaluation indicator,a comprehensive evaluation method integrates the continuous monitoring index (CV) and prediction results (dry density and compaction degree) from the ACGWO-RF algorithm.Fuzzy membership degree assessment is employed for the aforementioned aspects，and the D-S evidence theory，capable of managing multiple uncertainties，is utilized for evidence fusion.Finally，a multi-level feedback control mechanism is proposed within the rolling intelligent monitoring feedback control framework to provide on-site feedback control for rolling operations.Engineering examples demonstrate that compared to existing evaluation algorithms，the proposed method exhibits high precision (R=0.839)，strong generalization ability (R=0.793)，and robustness.The comprehensive evaluation method based on evidence theory can account for the uncertainty of limited random test pit data while reducing CV sensitivity to noise by 69.8%.Additionally，the multi-level feedback control mechanism effectively ensures on-site rolling quality. |
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