基于桩锤激励的浅层孔洞智能探测技术

Intelligent Detection Technology of Shallow Holes Based on Pile Hammer Excitation

本文提出了一种高安全性、高效率和低成本的智能孔洞探测技术。一方面,借鉴浅层地震波反射法的原理,以基础施工过程中的桩锤激震代替炸药作为激励源,并通过少量加速度传感器获得地表孔洞反射信号;另一方面,通过有限元模拟获得大量工况下的响应数据,提取地表加速度时程数据的内在特征并将其作为输入建立机器学习模型,从而实现高效率和低成本的孔洞探测技术。研究表明,在传感器布置方面:六传感器布置方案比四传感器布置方案的预测精度更高;此外在对比决策树、随机森林和KNN算法后发现,基于KNN算法的孔洞预测模型具有最高的准确率;最终对KNN算法进行调参后,在容许误差为2 m情况下,孔洞位置和直径的预测精确率可达98.1%。

This paper introduces a cave detection method with high safety,high efficiency and low cost.On the one hand,the principle of shallow seismic reflection method is used to obtain the reflection signal of caves from a small number of sensors on the ground surface,by using the pile hammer shock during foundation construction as excitation instead of explosives.On the other hand,through finite element simulation a large number of ground acceleration response data under various scenarios are obtained.And the intrinsic features of the time history data are extracted as the input for machine learning algorithms.Thus,a high-efficiency and low-cost detection method of caves can be realized.The results show that the k-nearest neighbor model based on the six-sensor placement strategy achieves the highest accuracy.The accuracy of the prediction of the location and diameter for caves can reach 98.1%with a tolerance error of 2 m after regulating parameters.