Challenges of bridge maintenance inspection

Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

CONDITION ASSESSMENT OF RC BRIDGES. INTEGRATING MACHINE LEARNING, PHOTOGRAMMETRY AND BIM

Abstract. The survey of building pathologies is focused on reading the state of conservation of the building, composed by the survey of constructive and decorative details, the masonry layering, the crack pattern, the degradation and the color recognition. The drawing of these representations is a time-consuming task, accomplished by manual work by skilled operators who often rely on in-situ analysis and on pictures. In this project three-dimensional an automated method for the condition survey of reinforced concrete spalling has been developed. To realize the automated image-based survey it has been exploited the Mask R-CNN neural network. The training phase has been executed over the original model, providing new examples of images with concrete cover detachments. At the same time, a photogrammetry process involved the images, in order to obtain a point cloud which acts as a reference to a Scan to BIM process. The BIM environment serves as a collector of information, as it owns the ontology to recreate entities and relationships. The information as extracted by neural network and photogrammetry serve to create the pictures which depict the concrete spalling in the BIM environment. A process of projecting information from the images to the BIM recreates the shapes of the pathology on the objects of the model, which becomes a decision support system for the built environment. A case study of a concrete beam bridge in northern Italy demonstrates the validity of the process.</p

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Computer vision-based structural assessment exploiting large volumes of images

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual information. Recent advances in computer vision to explore new sensors, sensing platforms and high-performance computing have shed light on the potential for vision-based visual assessment in civil engineering structures. The use of low-cost, high-resolution visual sensors in conjunction with mobile and aerial platforms can overcome spatial and temporal limitations typically associated with other forms of sensing in civil structures. Also, GPU-accelerated and parallel computing offer unprecedented speed and performance, accelerating processing the collected visual data. However, despite the enormous endeavor in past research to implement such technologies, there are still many practical challenges to overcome to successfully apply these techniques in real world situations. A major challenge lies in dealing with a large volume of unordered and complex visual data, collected under uncontrolled circumstance (e.g. lighting, cluttered region, and variations in environmental conditions), while just a tiny fraction of them are useful for conducting actual assessment. Such difficulty induces an undesirable high rate of false-positive and false-negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome the inherent challenges in using such images for visual assessment, high-level computer vision algorithms must be integrated with relevant prior knowledge and guidance, thus aiming to have similar performance with those of humans conducting visual assessment. Moreover, the techniques must be developed and validated in the realistic context of a large volume of real-world images, which is likely contain numerous practical challenges. In this dissertation, the novel use of computer vision algorithms is explored to address two promising applications of vision-based visual assessment in civil engineering: visual inspection, and visual data analysis for post-disaster evaluation. For both applications, powerful techniques are developed here to enable reliable and efficient visual assessment for civil structures and demonstrate them using a large volume of real-world images collected from actual structures. State-of-art computer vision techniques, such as structure-from-motion and convolutional neural network techniques, facilitate these tasks. The core techniques derived from this study are scalable and expandable to many other applications in vision-based visual assessment, and will serve to close the existing gaps between past research efforts and real-world implementations

Patch detection for pavement assessment

Pavement management systems rely on comprehensive up-to-date road condition data to provide effective decision support for short, medium and long term maintenance scheduling. However, the cost per mile of the existing condition data collection methods allows only for periodical surveys. This leads to long gaps between inspections and a focus on major roads over rural ones. Therefore, pavement condition monitoring systems that provide inexpensive frequent updates on the road condition are necessary. Such systems would require robust and automatic defect detection methods using low-cost sensors. In this paper, one such method is proposed for detecting road patches from video data acquired by the car's parking camera. A patch is initially detected based on its visual characteristics, which are: 1) it consists of a closed contour and 2) its texture is the same with the surrounding intact pavement. The patch is then passed to a kernel tracker in order to trace it in subsequent video frames. This way redetection is avoided and each patch is reported only once. The method was implemented in a C# prototype and tested with video data consisting of approximately 4000 frames collected from roads in Cambridge, UK. The results show that the suggested method has 84% precision and 96% recall.This material is based upon the work supported by the National Science Foundation (NSF Grant #1031329). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.This is the author accepted version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.autcon.2015.03.01

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