Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal ...Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process.展开更多
抗冲击性是现代舰用柴油机的设计指标之一,仿真分析是柴油机抗冲击研究的重要手段。将各种波形的加速度时域输入载荷转化为冲击谱,发现脉宽为10 m s的三角波加速度峰值与冲击谱等加速度线的值相同,建议将该波形作为设备抗冲击对比分析...抗冲击性是现代舰用柴油机的设计指标之一,仿真分析是柴油机抗冲击研究的重要手段。将各种波形的加速度时域输入载荷转化为冲击谱,发现脉宽为10 m s的三角波加速度峰值与冲击谱等加速度线的值相同,建议将该波形作为设备抗冲击对比分析的标准输入载荷。基于ABAQUS6.7软件,建立包含柴油机主干部件的整机多柔体抗冲击分析模型,分别实现了某V6型柴油机在额定工况和停机工况下受冲击的仿真计算。仿真结果表明,柴油机冲击响应特性是非线性的,额定工况下受冲击时应力响应值并非是停机情况下受冲击的应力响应值和额定工况下工作应力的简单叠加。柴油机曲轴、机体、飞轮壳和机脚的冲击强度都需要通过仿真进行校核。展开更多
基金supported by the National Natural Science Foundation of China(Nos.52378419 and 52478368).
文摘Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process.
文摘抗冲击性是现代舰用柴油机的设计指标之一,仿真分析是柴油机抗冲击研究的重要手段。将各种波形的加速度时域输入载荷转化为冲击谱,发现脉宽为10 m s的三角波加速度峰值与冲击谱等加速度线的值相同,建议将该波形作为设备抗冲击对比分析的标准输入载荷。基于ABAQUS6.7软件,建立包含柴油机主干部件的整机多柔体抗冲击分析模型,分别实现了某V6型柴油机在额定工况和停机工况下受冲击的仿真计算。仿真结果表明,柴油机冲击响应特性是非线性的,额定工况下受冲击时应力响应值并非是停机情况下受冲击的应力响应值和额定工况下工作应力的简单叠加。柴油机曲轴、机体、飞轮壳和机脚的冲击强度都需要通过仿真进行校核。
