This paper proposes a k-Nearest Neighbours (kNN) algorithm for locating and quantifying bridge damage based on the time-varying forced frequencies due to a moving truck. Eigenvalue analysis of a simplified vehicle-bridge coupled system, consisting of a three-axle rigid truck model and a simply supported finite element beam model, shows how the eigenfrequencies of the coupled system vary with the locations of the vehicle and with the damage represented by a stiffness loss. The computational efficiency of eigenvalue analysis is exploited to generate a vast sample of patterns for training a kNN algorithm. In the field, acceleration due to the crossing of a test vehicle would be measured and analysed using a time–frequency signal processing tool to obtain the instantaneous frequencies. The crossing must take place at a low speed to achieve sufficiently high resolution and to minimise deviations from the eigenvalue solution. Then, the kNN algorithm searches for the patterns of forced eigenfrequencies that are closest to the on-site instantaneous frequencies to determine the location and severity of the damage. For theoretical testing purposes, field measurements are simulated here using coupled equations of motion and dynamic transient analysis.

本文提出了一种基于k的最近邻（kNN）算法，用于基于随卡车移动的时变强制频率来定位和量化桥梁损伤。简化的车桥耦合系统的本征值分析，包括三轴刚性卡车模型和简单支持的有限元梁模型，显示了耦合系统的本征频率如何随车辆的位置以及所代表的损坏而变化刚度损失。利用特征值分析的计算效率来生成用于训练kNN算法的大量模式样本。在现场，将使用时频信号处理工具来测量和分析由于测试车辆的穿越而引起的加速度，以获得瞬时频率。交叉必须以低速进行，以实现足够高的分辨率并最小化与特征值解的偏差。然后，kNN算法搜索最接近现场瞬时频率的强制本征模式，以确定损坏的位置和严重程度。为了进行理论测试，此处使用运动和动态瞬态分析的耦合方程对现场测量值进行仿真。