A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

Structural Damage Detection Using Machine Learning Techniques

In order to ensure the integrity of the structure, timely and accurate detection and identification of structural damage during and after an extreme event or over the lifetime of a structure is a very important for safety and economic resons. Structural damage detection and identification techniques can be generally classified into two main categories based on whether they use dynamic or static test data. This research project will use convolutional neural networks, which will focus on leveraging the capabilities of several models and toolkits, including AlexNet, Tensorflow and Pytorch. We will establish baseline performance metrics for an existing CNN-based program using a static image set of classified bolt, corrosion and crack damages. Investigate the application of machine learning methods to improve the performance of the CNN-based program on identifying damage. Investigate the performance of the modified CNN-program on new image data set that are collected real-time using a drone