Integrated Condition Assessment of Subway Networks Using Computer Vision and Nondestructive Evaluation Techniques

Subway networks play a key role in the smart mobility of millions of commuters in major metropolises. The facilities of these networks constantly deteriorate, which may compromise the integrity and durability of concrete structures. The ASCE 2017 Report Card revealed that the condition of public transit infrastructure in the U.S. is rated D-; hence a rehabilitation backlog of $90 billion is estimated to improve transit status to good conditions. Moreover, the Canadian Urban Transit Association (CUTA) reported 56.6 billion CAD in infrastructure needs for the period 2014-2018. The inspection and assessment of metro structures are predominantly conducted on the basis of Visual Inspection (VI) techniques, which are known to be time-consuming, costly, and qualitative in nature. The ultimate goal of this research is to develop an integrated condition assessment model for subway networks based on image processing, Artificial Intelligence (AI), and Non-Destructive Evaluation (NDE) techniques. Multiple image processing algorithms are created to enhance the crucial clues associated with RGB images and detect surface distresses. A complementary scheme is structured to channel the resulted information to Artificial Neural Networks (ANNs) and Regression Analysis (RA) techniques. The ANN model comprises sequential processors that automatically detect and quantify moisture marks (MM) defects. The RA model predicts spalling/scaling depth and simulates the de-facto scene by developing a hybrid algorithm and interactive 3D presentation. In addition, a comparative analysis is performed to select the most appropriate NDE technique for subway inspection. This technique is applied to probe the structure and measure the subsurface defects. Also, a novel model for the detection of air voids and water voids is proposed. The Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Monte Carlo Simulation (MCS) are streamlined through successive operations to create the integrated condition assessment model. To exemplify and validate the proposed methodology, a myriad of images and profiles are collected from Montréal Metro systems. The results ascertain the efficacy of the developed detection algorithms. The attained recall, precision, and accuracy for MM detection algorithm are 93.2%, 96.1%, and 91.5% respectively. Whereas for spalling detection algorithm, are 91.7%, 94.8%, and 89.3% respectively. The mean and standard deviation of error percentage in MM region extraction are 12.2% and 7.9% respectively. While for spalling region extraction, they account for 11% and 7.1% respectively. Subsequent to selecting the Ground Penetrating Radar (GPR) for subway inspection, attenuation maps are generated by both the amplitude analysis and image-based analysis. Thus, the deteriorated zones and corrosiveness indices for subway elements are automatically computed. The ANN and RA models are validated versus statistical tests and key performance metrics that indicated the average validity of 96% and 93% respectively. The air/water voids model is validated through coring samples, camera images, infrared thermography and 3D laser scanning techniques. The validation outcomes reflected a strong correlation between the different results. A sensitivity analysis is conducted showing the influence of the studied subway elements on the overall subway condition. The element condition index using neuro-fuzzy technique indicated different conditions in Montréal subway systems, ranging from sound concrete to very poor, represented by 74.8 and 35.1 respectively. The fuzzy consolidator extrapolated the subway condition index of 61.6, which reveals a fair condition for Montréal Metro network. This research developed an automated tool, expected to improve the quality of decision making, as it can assist transportation agencies in identifying critical deficiencies, and by focusing constrained funding on most deserving assets

An image-based data model for subway condition assessment

The Canadian Urban Transit Association (CUTA) estimated that transit infrastructure needed a total of 53 Billion Canadian Dollars in 2013. Subway networks form an essential part of the public transportation infrastructure. Several surface defects may develop on subway infrastructure facilities, of which the most commonly identified are cracks, scaling, spalling, delamination, moisture marks, and efflorescence. These distresses participate not only in degrading the structure aesthetically, but in increasing the deterioration mechanisms of its components, taking into account the severe environmental conditions and continuous heavy loads that the structure is subjected to during its service life. High deterioration rates may cause the closure of subway system, therefore condition assessment of subway networks represents a crucial yet challenging task in the sustainability of a sound concrete infrastructure. Visual inspection techniques are considered the principal methods used in the condition evaluation of civil infrastructure. These methods are time-consuming, expensive, and depend inherently on subjective criteria. Several models have been proposed by previous researchers to assess the condition of subway systems. However, all of the developed methods were dependent on the visual inspection reports, hence they lacked the objectivity in quantifying and estimating the severity of defects. Therefore, a robust model that can detect the distresses and compute their severity needs to be developed. This paper defines the details of the recently introduced procedure based on image processing and assessment techniques. A five phased process is presented for accurate condition assessment of subway networks. The developed methodology utilizes data acquisition tools for collecting images of different elements in subway networks. Multiple algorithms are utilized to detect, interpret and measure surface defects, such as binary transformation, histogram equalization, image dilation, and hole filling. A case study from Montreal subway system was used to exemplify the application of the developed method. The results prove the potential benefits of the proposed methodology in identifying and quantifying surface defects. This research concludes the reliability of image-based data model in terms of accuracy, efficiency, and ease of analysis.Non UBCUnreviewedFacultyOthe

Special Issue “Ground Penetrating Radar (GPR) Applications in Civil Infrastructure Systems”

This Special Issue includes a collection of papers that address the practical applications of GPR to various civil infrastructure systems [...

Water pipe failure prediction and risk models: state-of-the-art review

This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

First Expansion of the Public Tomato Brown Rugose Fruit Virus (ToBRFV) Nextstrain Build; Inclusion of New Genomic and Epidemiological Data

Tomato brown rugose fruit virus (ToBRFV) is a tobamovirus that was first detected in Israel and Jordan following an outbreak of a new disease infecting tomato in 2014. Since then, the virus has been reported from all continents except Oceania and Antarctica. In response to the first finding of the virus in The Netherlands, the Dutch National Plant Protection Organization created a ToBRFV Nextstrain build (v1). In this report, we announce 47 new (near) complete ToBRFV genomes and the generation of the new ToBRFV Nextstrain (v2) build containing 118 ToBRFV genomes with associated geographic and epidemiological data. Examples of utilization of the genomic sequences are presented, and we report the first sequence from South America and present a novel hypothesis on the possible ToBRFV center of origin.[Graphic: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license

Rapid flooding-induced adventitious root development from preformed primordia in solanum dulcamara

Contains fulltext : 123473.pdf (publisher's version ) (Open Access