Motion Trajectories of Over-Height Vehicles for Warning Drivers

Collision of over-height vehicles with low bridges and tunnels occur with high frequency in the UK as many structures were built at a time when there was less moving traffic on the roadway. These older bridges are now considered at risk of vehicular strikes due to its low clearance height (less than 16 feet 6 inches or 5.03 metres). While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we proposed an extension of the work done to minimise false detections due to wind by using a constraint-based method to track motion trajectories to improve the overall performance of the system. The dataset consists of 102 over-height vehicles recorded at 25 fps. The paper compares feature detectors to optimally track vehicle trajectories and analyses its motion to accurately classify positive detections. The final validation yields a performance of 94.5% recall and 91.1% precision.Career Integration Grants (CIG) - Marie Curie Action

Digital twinning of railway overhead line equipment from airborne lidar data

The automated generation of geometry-only digital twins of Overhead Line Equipment (OLE) system in existing railways from point clouds is an unsolved problem. Currently, this process is highly reliant upon manual inputs, needing 10 times more labour hours than scanning the physical asset. The resulting modelling cost counteracts the expected benefits of the digital twin. We tackle this challenge using a novel model-driven method that exploits the highly regulated and standardised nature of railways. It starts by restricting the search for OLE elements relative to point clusters of the railway masts. The resulting point clusters of the OLE elements are then converged with various parametric models of different catenary configurations to verify the presence of OLE elements and to find the best possible fit. The method outputs a geometry-only digital twin of the OLE system in Industry Foundation Classes (IFC) format. The method was tested on an 18 km railway point cloud and achieves overall detection rates of 93.2% F1 score for OLE cables and 98.1% F1 score for other OLE elements. The accuracy of the generated model is evaluated using distance-based metrics between the ground truth model and the automated model. The average modelling distance is 3.82 cm Root Mean Square Error (RMSE) for all 18 km dataCambridge Commonwealth, European & International Trust Bentley Systems UK Plc

Detection of Walls, Floors, and Ceilings in Point Cloud Data

This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.229The successful implementation of Building Information Models (BIMs) for facility management, maintenance and operation is highly dependent on the ability to generate such models for existing assets. Generating such BIMs typically requires laser scanning to acquire point clouds and significant post-processing to register the clouds, replace the points with BIM objects, assign semantic relationships and add any additional properties, such as materials. Several research efforts have attempted to reduce the post-processing manual effort by classifying the structural elements and clutter in isolated rooms. They have not however examined the complexity of a whole building. In this paper, we propose a robust framework that can automatically process the point cloud of an entire building, possibly with multiple floors, and classify the points belonging to floors, walls and ceilings.. We first extract the planar surfaces by segmenting the point cloud, and then we use contextual reasoning, such as height, orientation, relation to other objects, and local statistics like point density in order to classify them into objects. Experiments were conducted on a registered point cloud of an office building. The results indicated that almost all of the walls and floors/ceilings were correctly clustered in the point cloud.The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreements n° 247586 ("BIMAutoGen") and n° 334241 ("INFRASTRUCTUREMODELS")

A Framework for Automated Pavement Condition Monitoring

Pavement condition monitoring is mainly performed manually. Inspectors are driving or walking the road network bare eyed to look for irregularities. Moreover, processing the collected data for understanding the road condition is also a manual task. In this paper a framework that automates the process is presented. Video data collected from the car’s parking camera is utilized to detect defects in frames. Simultaneously, elevation signals collected from accelerometers attached to the car are processed to reconstruct the profile of the road and detect defects associated with its z-axis, such as bumps. A GPS device is synchronized with the other sensors to acquire the data’s geolocation. Detected defects are then classified according to their type and their severity is assessed. All information is then transferred via 4G network to a central server, where the Road Condition Index of road segments necessary to classify roads is calculated. Finally, everything is saved in a Pavement Management System. Preliminary results on the processing of video data demonstrate the frameworks’ promising application. The initial identification of frames including defects produces an accuracy of 96% and approximately 97% precision. Further experiments on such frames, aiming at the detection of potholes, patches and three different types of cracks result in over 84% overall accuracy and over 85% precision.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via http://dx.doi.org/10.1061/9780784479827.07

A vision-based method for on-road truck height measurement in proactive prevention of collision with overpasses and tunnels

This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0926580514002167.Over-height trucks are continuously striking low clearance overpasses and tunnels. This has led to significant damage, fatalities, and inconvenience to the public. Smart systems can automatically detect and warn oversize trucks, and have been introduced to provide the trucks with the opportunity to avoid a collision. However, high cost of implementing these systems remains a bottleneck for their wide adoption. This paper evaluates the feasibility of using computer vision to detect over-height trucks. In the proposed method, video streams are collected from a surveillance camera attached on the overpass/tunnel, and processed to measure truck heights. The height is measured using line detection and blob tracking which locate upper and lower points of a truck in pixel coordinates. The pixel coordinates are then translated into 3D world coordinates. Proof-of-concept experiment results signify the high performance of the proposed method and its potential in achieving cost-effective monitoring of over-height trucks in the transportation system. The limitations and considerations of the method for field implementation are also discussed.This material is based upon work supported by West Virginia University, Myongji University, and University of Cambridge

Automated Damage Index Estimation of Reinforced Concrete Columns for Post-Earthquake Evaluations

In emergency scenarios, immediate reconnaissance efforts are necessary. These efforts often take months to complete in full. While underway, building occupants are unable to return to their homes/businesses, and thus, the impact on the society of the disaster-stricken region is increased. In order to mitigate the impact, researchers have focused on creating a more efficient means of assessing the condition of buildings in the post-disaster state. In this paper, a machine vision-based methodology for real-time post-earthquake safety assessment is presented. A novel method of retrieving spalled properties on reinforced concrete (RC) columns in RC frame buildings using image data is presented. In this method, the spalled region is detected using a local entropy-based approach. Following this, the depth properties are retrieved using contextual information pertaining to the amount and type of reinforcement which is exposed. The method is validated using a dataset of damaged RC column images.This material is based in part upon work supported by the National Science Foundation under Grant Numbers CMMI-1034845 and CMMI-0738417.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)ST.1943-541X.000120

Fragility curves for non-ductile reinforced concrete frames that exhibit different component response mechanisms

Around the world, a large percentage of buildings in regions of high seismicity are older, non-ductile reinforced concrete. To assess the risk posed by these buildings, fragility functions are required to define the likelihood that these buildings will sustain damage and collapse under earthquake loading. This paper presents the initial phase of a research effort to develop fragility functions for non-ductile concrete frames using numerical simulation; the research presented in this paper focuses on development of the numerical model and application of the model to develop fragility functions for a prototype non-ductile concrete frame. To enable numerical simulation of concrete frame buildings, response models for beam–column joints and columns are developed to provide (1) appropriate simulation of component response and, thereby, reliable assessment of risk and (2) computational efficiency and robustness. These new models are developed using existing experimental data, build on response models proposed by others, and employ component and material models available in the OpenSees analysis platform (http://opensees.berkeley.edu). A new beam–column joint model combines a new expression for joint strength and newly developed cyclic response parameters; a new column response model includes a new shear-strength model and newly developed cyclic response parameters. Numerical models of a prototype non-ductile concrete frame are developed that include simulation of one or more of the following characteristics: (1) rigid beam–column joint, (2) nonlinear joint shear response, (3) nonlinear joint shear and bond–slip response, and (4) column shear failure. Dynamic analyses are performed using these frame models and a suite of ground motions; analysis results are used to develop fragility curves. Fragility curves quantify the vulnerability of the frame and provide understanding of the impact of different component failure mechanisms on frame vulnerability.This research was supported by the National Science Foundation under NSF Grant # 1000700.This is the accepted manuscript of a paper published in Engineering Structures (J-S Jeon, LN Lowes, R DesRoches, I Brilakis, Engineering Structures 2015, 85, 127–143

Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system

Construction is an inherently less safe sector than other sectors because it exposes workers to harsh and dangerous working environments. The nature of the construction industry results in a comparatively high incidence of serious injuries and death caused by falls from a height, musculoskeletal disorders and being struck by objects. This paper presents a new concept that can tackle this problem in the future. The central hypothesis of this study is that it is possible to eliminate injuries if we move the human construction worker off-site and remotely link his/her motions to a Robotic Construction Worker (RCW) on-site. As a first steppingstone towards this ultimate goal, two systems essential for the RCW were developed in this study. First, a novel system that combines 3D body and hand position tracking was developed to capture the movements of human construction worker. This combination of tracking enables the capture of changes in the orientations and articulations of the entire human body. Second, a real-time simulation system that connects a human construction worker off-site to a virtual RCW was developed to demonstrate the proposed concept in a variety of construction scenarios. The simulation results demonstrate the future viability of the RCW concept and indicate the promise of this system for eliminating the health and safety risks faced by human construction workers

State of research in automatic as-built modelling

This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.aei.2015.01.001Building Information Models (BIMs) are becoming the official standard in the construction industry for encoding, reusing, and exchanging information about structural assets. Automatically generating such representations for existing assets stirs up the interest of various industrial, academic, and governmental parties, as it is expected to have a high economic impact. The purpose of this paper is to provide a general overview of the as-built modelling process, with focus on the geometric modelling side. Relevant works from the Computer Vision, Geometry Processing, and Civil Engineering communities are presented and compared in terms of their potential to lead to automatic as-built modelling.We acknowledge the support of EPSRC Grant NMZJ/114,DARPA UPSIDE Grant A13–0895-S002, NSF CAREER Grant N. 1054127, European Grant Agreements No. 247586 and 334241. We would also like to thank NSERC Canada, Aecon, and SNC-Lavalin for financially supporting some parts of this research

3D Matching of Resource Vision Tracking Trajectories

Three-dimensional (3D) paths of resources have been proposed in construction management, as an efficient way for measuring labor productivity. These paths are either extracted by using sensors such as global positioning system (GPS), radio frequency identification (RFID), and ultra-wideband (UWB), or based on cameras placed at jobsites for surveillance purposes. However, the tag-based methods are seriously limited by privacy conflicts since they are not welcome from the personnel. On the other hand, the computer vision based methods have not achieved full automation in measuring labour productivity because they require prior knowledge of the type of tasks performed in specific working zones. This is associated with the lack of depth information. For this purpose, this paper proposes a computationally efficient computer vision method for matching construction workers across different frames. Entity matching is a process that corresponds to a compulsory step prior to the calculation of the 3D position. The proposed matching method, is based on epipolar geometry, template and motion similarity features. The main result of this process is to provide a method for the acquisition of the 3D paths that compose the detailed profile of a construction activity in terms of both time and space.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via https://doi.org/10.1061/9780784479827.17