A Systematic Literature Survey of Unmanned Aerial Vehicle Based Structural Health Monitoring

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs based on their aerodynamics, payload, design of build, and its applications. Further, the thesis presents the payload product line to facilitate the SHM tasks, details the different applications of UAVs exploited in the last decade to support civil structures, and discusses the critical challenges faced in UASHM applications across various domains. Finally, the thesis presents two artificial neural network-based structural damage detection models and conducts a detailed performance evaluation on multiple platforms like edge computing and cloud computing

Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey

With the rapid advance in camera sensor technology, the acquisition of high-resolution images or videos has become extremely convenient and cost-effective. Computer vision that extracts semantic knowledge directly from digital images or videos, offers a promising solution for non-contact and full-field structural vibration measurement and condition assessment. Unmanned aerial vehicles (UAVs), also known as flying robots or drones, are being actively developed to suit a wide range of applications. Taking advantage of its excellent mobility and flexibility, camera-equipped UAV systems can facilitate the use of computer vision, thus enhancing the capacity of the structural condition assessment. The current article aims to provide a concise survey of the recent progress and applications of UAV-based computer vision in the field of structural dynamics. The different aspects to be discussed include the UAV system design and algorithmic development in computer vision. The main challenges, future trends, and opportunities to advance the technology and close the gap between research and practice will also be stated

INSPIRE Newsletter Fall 2018

https://scholarsmine.mst.edu/inspire-newsletters/1003/thumbnail.jp

Lightweight Real-time Detection of Components via a Micro Aerial Vehicle with Domain Randomization Towards Structural Health Monitoring

Civil structural component detection plays an integral role in Structural Health Monitoring (SHM) pre and post-construction. Challenges including but not limited to labor-intensiveness, cost, and time constraints associated with traditional methods make it a less opti-mal approach in SHM. Despite the success of deep convolutional neural networks in diverse detection problems, the required computational resources are a challenge. This has led to rendering a chunk of resource-constrained edge nodes less applicable with deep convolutional neural networks. In this paper, a computational-efficient deep convolutional neural network is presented based on Gabor filters and a color Canny edge detector. Generic Gabor filters are generated and used as initializers in the computational-efficient deep convolutional neural network presented, afterward trained on building components data. Next, extensive offline and online experimentation with a resource-constrained edge node is conducted and evaluated using diverse metrics. The computational-efficient detection model demonstrates to be effective in detection and via NVIDIA GPU profiler, we observe conservation of around 30% of computational resources during training. The computational-efficient detection model adduces almost a 3% mean average precision higher than two state-of-the-art detectors and records a promising frame processing rate during the online experimentation

Surface and Sub-Surface Analyses for Bridge Inspection

The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

INSPIRE Newsletter Spring 2018

https://scholarsmine.mst.edu/inspire-newsletters/1002/thumbnail.jp

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