地下洞室边墙爆破振动传播衰减规律研究

基于桃花嘴矿区地下洞室爆破振动现场试验的实测数据,发现了地下洞室爆破开挖诱发边墙质点峰值振动速度传播衰减的特点,即爆源近区和边墙中心区域具有更大的放大效应。建立了四边简支板的动力学模型,进一步结合量纲分析法,推导出考虑边墙四边约束的地下洞室爆破边墙质点峰值振动速度预测公式。将实测数据分别代入萨道夫斯基公式和推导公式进行线性回归运算,得到萨道夫斯基公式预测值和推导公式预测值的平均误差分别为30.7%和5.1%。结果表明,该公式能较准确地反映地下洞室爆破边墙质点峰值振动速度存在的放大效应,特别可以精确预测放大效应显著区域的爆破振动峰值速度,为更好地控制边墙爆破振动损伤提供了参考价值。     

Wavelet–copula‐based mutual information for rainfall forecasting applications

Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.