Prediction of blast loads on bridge girders based on PCA-BPNN
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摘要: 人工智能方法是预测爆炸荷载的新手段,但现有方法主要用于预测爆炸冲击波的超压峰值或冲量,而预测反射超压时程的研究不多。针对这一问题,以平面冲击波绕射桥梁主梁为对象,提出了一种基于主成分分析(PCA)和误差反向传播神经网络(BPNN)的桥梁表面反射超压时程的预测模型。该预测模型利用PCA降维处理时程数据,基于多任务学习的BPNN算法,提出了考虑超压峰值和最大冲量影响的损失函数,使模型能有效预测不同入射强度下的桥梁冲击波荷载时程。通过比较多任务学习模型、多输入单输出模型和多输入多输出模型等三种BPNN模型,发现多任务学习模型的预测精度最高,而多输入多输出模型的预测能力较差;采用多任务学习模型预测得到的桥梁表面各测点位置的反射超压时程、超压峰值精度较高,R2分别为0.790和0.985,作用在箱梁上的合力时程和扭矩时程预测值也与真实值较为吻合。同时,该模型在对内插值预测的表现优于外推值预测,但其在预测外推值方面同样展现出了一定的能力。
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Abstract: Facing the challenges of accurate and effective prediction under extreme loads, machine learning has gradually demonstrated its potential to replace traditional methods. Existing approaches primarily focus on predicting the peak overpressure or impulse of explosive shock waves, with limited research on predicting the reflected overpressure time history. Load-time history prediction encompasses not only the peak overpressure but also embraces various multi-dimensional information including duration, waveform, and impulse, thereby offering a more comprehensive depiction of the dynamic temporal and spatial characteristics of shock waves. To address this issue, a prediction model for bridge surface reflected overpressure time history is proposed, targeting a planar shock wave diffracting around a bridge section. This model is based on Principal Component Analysis (PCA) and Backpropagation Neural Network (BPNN) algorithm with multi-task learning. A loss function considering the impact of peak overpressure and maximum impulse is introduced to fully consider the potential correlations between different modes after PCA dimension reduction. This enables the
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