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基于云-模糊模型的堆石坝施工质量评估(英文) 被引量:5
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作者 Fei WANG Deng-hua ZHONG +2 位作者 Yu-ling YAN Bing-yu REN bin-ping wu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2018年第4期289-303,共15页
目的:施工质量对于大坝建设期及运行期的安全至关重要。由于施工过程中的信息不完备及碾压质量与影响因素之间的关系并不是完全确定等原因,传统的评估方法很少考虑不确定性对施工质量的影响。本文旨在探讨考虑不确定性影响的碾压质量评... 目的:施工质量对于大坝建设期及运行期的安全至关重要。由于施工过程中的信息不完备及碾压质量与影响因素之间的关系并不是完全确定等原因,传统的评估方法很少考虑不确定性对施工质量的影响。本文旨在探讨考虑不确定性影响的碾压质量评估方法,改善施工质量评估的可信性。创新点:1.通过研究模糊神经网络与径向基神经网络,结合云模型建立云-模糊模型;2.建立施工质量三指标体系评价方法。方法:1.通过碾压质量实时监控系统和现场试坑试验获取参数数据;2.通过云分析,建立云-模糊模型;3.对比不同的模型,验证云-模糊模型的可行性;4.利用验证的云-模糊模型对大坝施工仓面进行压实干密度预测;5.计算评价体系的三指标,对施工质量进行评估。结论:1.云-模糊模型不但能在精度上满足预测要求,而且能够综合考虑施工质量与影响因素之间的不确定性关系;2.云-模糊评价方法弥补了传统评价方法仅追求精度的单一性,使得施工质量评价更符合客观规律;3.提出的施工质量三指标评价体系充实了传统的评价方法,能够更客观地指导实际工程建设。 展开更多
关键词 堆石坝 云模型 不确定性 施工质量评价
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基于实时监控的碾压混凝土坝施工仿真(英文) 被引量:4
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作者 Qian-wei WANG Deng-hua ZHONG +2 位作者 bin-ping wu Jia YU Hao-tian CHANG 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2018年第5期367-383,共17页
目的:碾压混凝土坝施工过程中施工仿真参数会随着施工现场环境变化而变化。本文探讨实时监控方法获取的施工信息对施工进度仿真的影响,研究碾压混凝土坝施工仿真参数自适应更新方法,提高施工仿真的精度。创新点:1.通过碾压混凝土坝施工... 目的:碾压混凝土坝施工过程中施工仿真参数会随着施工现场环境变化而变化。本文探讨实时监控方法获取的施工信息对施工进度仿真的影响,研究碾压混凝土坝施工仿真参数自适应更新方法,提高施工仿真的精度。创新点:1.通过碾压混凝土坝施工信息实时获取技术,分析计算碾压混凝土坝施工仿真参数;2.利用贝叶斯更新技术对施工仿真参数进行更新;3.利用模糊均生函数对坝区短期降雨量进行预测;4.建立基于实时监控的碾压混凝土坝施工仿真模型,对碾压混凝土坝施工过程进行仿真并与实际施工进度对比。方法:1.通过实地采集,获取碾压混凝土坝施工过程中实时施工信息(图2);2.通过理论推导,构建施工仿真参数先验分布均值和方差与后验分布均值和方差之间的关系,得到施工仿真参数更新方案(公式(16)和(17));3.通过理论推导,利用已知坝区降雨量数据预测未来短期内的降雨情况(图5);4.通过仿真模拟,得到施工仿真参数更新后的仿真成果并将其与实际施工进行对比,验证本方法的有效性和准确性。结论:1.施工仿真参数的准确性对碾压混凝土坝施工仿真结果准确性有很大影响;2.可以利用贝叶斯更新技术对施工仿真中的仿真参数进行更新,利用模糊均生函数对坝区短时期内降雨量进行预测;3.运用基于实时监控的碾压混凝土坝施工仿真方法对碾压混凝土坝施工过程进行仿真,仿真结果与实际施工进度之间的偏差明显减少,仿真准确性明显提高。 展开更多
关键词 碾压混凝土坝 施工仿真 实时监控 贝叶斯更新 模糊均生函数
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Dynamic time-cost-quality tradeoff of rockfill dam construction based on real-time monitoring 被引量:4
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作者 Deng-hua ZHONG Wei HU +2 位作者 bin-ping wu Zheng LI Jun ZHANG 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2017年第1期1-19,共19页
Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or des... Time, cost, and quality are three key control factors in rockfill dam construction, and the tradeoff among them is important. Research has focused on the construction time-cost-quality tradeoff for the planning or design phase, built on static empirical data. However, due to its intrinsic uncertainties, rockfill dam construction is a dynamic process which requires the tradeoffto adjust dynamically to changes in construction conditions. In this study, a dynamic time-cost-quality tradeoff (DTCQT) method is proposed to balance time, cost, and quality at any stage of the construction process. A time-cost-quality tradeoff model is established that considers time cost and quality cost. Time, cost, and quality are dynamically estimated based on real-time monitoring. The analytic hierarchy process (AHP) method is applied to quantify the decision preferences among time, cost, and quality as objective weights. In addition, an improved non-dominated sorting genetic algorithm (NSGA-II) coupled with the technique for order preference by similarity to ideal solution (TOPSIS) method is used to search for the optimal compromise solution. A case study project is analyzed to demonstrate the applicability of the method, and the efficiency of the proposed optimization method is compared with that of the linear weighted sum (LWS) and NSGA-II. 展开更多
关键词 Dynamic time-cost-quality tradeoff Rockfill dam construction Real-time monitoring Decision preferences
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A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation 被引量:1
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作者 Fei LV Jia YU +3 位作者 Jun ZHANG Peng YU Da-wei TONG bin-ping wu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2022年第12期1027-1046,共20页
Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity an... Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively. 展开更多
关键词 Drilling efficiency PREDICTION Earth-rock excavation Stacking-based ensemble learning Improved cuckoo search optimization(ICSO)algorithm Comprehensive effects of various factors Hyper-parameter optimization
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