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AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 1 September 2024 | Viewed by 3584

Special Issue Editors

Dr. Da Hu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Kennesaw State University, Marietta, GA 30060, USA
Interests: automation in construction; robotics and sensing; smart infrastructure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Niannian Wang

E-Mail Website
Guest Editor
School of Water Conservancy Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: civil engineering; damage detection; safety evaluation; numerical simulation; crowd sensing; deep learning; unmanned aerial vehicle; monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue of Sustainability, entitled "AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance", we explore the latest advancements and applications of artificial intelligence (AI) in the realm of civil infrastructure systems. As the demand for efficient, resilient, and eco-friendly infrastructure continues to grow, AI-driven innovations are playing increasingly crucial roles in revolutionizing the ways we maintain, operate, and monitor our built environment. This Issue delves into various topics related to the management, monitoring, and inspection of civil infrastructure systems. These topics include leveraging AI-driven decision making, data analytics, and predictive modeling to improve resource management, enhance safety and reliability, and promote more efficient use of infrastructure assets across different sectors.

Contributors should discuss the application of AI in diverse aspects of civil infrastructure systems, such as structural health monitoring, automated defect detection, and predictive maintenance for assets like bridges, tunnels, pipelines, and roads. These articles will showcase pioneering research and include case studies of the ways in which AI-powered tools can enhance the safety, reliability, and lifespan of our built environment. Additionally, the Issue will explore the role of AI in optimizing the operation and planning of infrastructure systems, as well as its impact on urban planning, city management, and public services. By integrating AI technologies into these areas, we can create sustainable and people-centric urban environments that are better prepared to handle the challenges of climate change, urbanization, and resource constraints. Through the contributions of experts, researchers, and practitioners from various disciplines, this Special Issue aspires to inspire the development of new ideas and collaborations that will drive the future of AI-powered sustainable and intelligent civil infrastructure systems, ultimately promoting a more resilient and sustainable world for all.

Dr. Da Hu
Prof. Dr. Niannian Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • civil infrastructure
  • infrastructure inspection
  • infrastructure monitoring
  • structural health monitoring
  • data-driven decision making
  • automated defect detection

Published Papers (4 papers)

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Research

21 pages, 3269 KiB  
Article
Analysis of Estimation of Soundness and Deterioration Factors of Sewage Pipes Using Machine Learning
by Taiki Suwa, Makoto Fujiu, Yuma Morisaki and Tomotaka Fukuoka
Sustainability 2023, 15(22), 16081; https://doi.org/10.3390/su152216081 - 18 Nov 2023
Cited by 1 | Viewed by 730
Abstract
In Japan, there are a massive number of sewage pipes buried in the ground. In order to operate sustainable sewerage systems, it is necessary to estimate the soundness of sewage pipes accurately and to conduct repairs and other measures according to the soundness [...] Read more.
In Japan, there are a massive number of sewage pipes buried in the ground. In order to operate sustainable sewerage systems, it is necessary to estimate the soundness of sewage pipes accurately and to conduct repairs and other measures according to the soundness of the pipes. In previous studies, statistical and machine learning methods have been used to estimate the soundness of sewage pipes, but all of these studies formulated the soundness of sewage pipes as a binary classification problem (e.g., good or poor). In contrast, this study attempted to predict the soundness of sewage pipes in more detail by setting up four classes of pipe soundness. Inspection data of sewage pipes in City A were used as training data, and XGBoost was used as the machine learning model. Machine learning models have a high prediction performance, but the uncertainty of the prediction basis is an issue. In this study, SHAP (Shapley additive explanations), an Explainable AI method, was used to interpret the model to clarify the influence of sewer pipe specifications (e.g., pipe age) and topographical specifications (e.g., annual precipitation) on the prediction, and to extract deterioration factors. By interpreting the model using SHAP, it was possible to quantify whether factors such as pipe age and pipe length have a positive or negative impact on the deterioration of sewage pipes. Previous studies using machine learning methods have not clarified whether factors have a positive or negative effect on deterioration. The knowledge on deterioration factors obtained in this study may provide useful information for the sustainable operation of sewage systems. Full article
(This article belongs to the Special Issue AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance)
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17 pages, 13745 KiB  
Article
Evaluation of the Coupling Synergy Degree of Inland Ports and Industries along the Yangtze River
by Yu Wan, Chengfeng Huang, Wenwen Zhou and Mingwu Liu
Sustainability 2023, 15(21), 15578; https://doi.org/10.3390/su152115578 - 02 Nov 2023
Viewed by 564
Abstract
This paper takes the two subsystems of inland ports and industries along the Yangtze River as a composite system as a research object, providing insight into the influence of the development efficiency of inland ports along the Yangtze River and the coupling and [...] Read more.
This paper takes the two subsystems of inland ports and industries along the Yangtze River as a composite system as a research object, providing insight into the influence of the development efficiency of inland ports along the Yangtze River and the coupling and synergy degree of industry. Firstly, the coupling synergy effect of the inland port industry is qualitatively expounded, and secondly, the entropy weight TOPSIS method is used to construct a coupling coordination model to analyze the measurement of the development efficiency and industrial coupling synergy relationship between inland ports along the Yangtze River. Twelve cities along the Yangtze River were selected as examples, the coupling synergy degree of their ports was evaluated by referring to the comprehensive port development synergy index from 2010 to 2019, and the development levels and evolution processes of the different cities were compared and analyzed. The coupling synergy development of the inland port industry along the Yangtze River can provide a basis for the problems and countermeasures faced by the integrated development of the inland port industry along the Yangtze River. Full article
(This article belongs to the Special Issue AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance)
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22 pages, 12656 KiB  
Article
A Methodological Approach for Data Collection and Geospatial Information of Healthy Public Spaces in Peripheral Neighborhoods—Case Studies: La Bota and Toctiuco, Quito, Ecuador
by Ana Medina, Diana Mosquera and Francisco Alejandro Gallegos
Sustainability 2023, 15(21), 15553; https://doi.org/10.3390/su152115553 - 02 Nov 2023
Viewed by 1075
Abstract
Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation in healthy cities. Latterly, there have been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces [...] Read more.
Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation in healthy cities. Latterly, there have been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim toward pioneering research in urban studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador—La Bota and Toctiuco—to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality for these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights into potential healthy spaces and information to urban planners and policymakers in decision-making for healthy urban infrastructure. Full article
(This article belongs to the Special Issue AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance)
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18 pages, 5653 KiB  
Article
BiLSTM for Predicting Post-Construction Subsoil Settlement under Embankment: Advancing Sustainable Infrastructure
by Liyang Wang, Taifeng Li, Pengcheng Wang, Zhenyu Liu and Qianli Zhang
Sustainability 2023, 15(20), 14708; https://doi.org/10.3390/su152014708 - 10 Oct 2023
Viewed by 626
Abstract
The load and settlement histories of stage-constructed embankments provide critical insights into long-term surface behavior under embankment loading. However, these data often remain underutilized in predicting post-construction settlement in the absence of geotechnical subsoil characterization. To address this limitation, the current study integrates [...] Read more.
The load and settlement histories of stage-constructed embankments provide critical insights into long-term surface behavior under embankment loading. However, these data often remain underutilized in predicting post-construction settlement in the absence of geotechnical subsoil characterization. To address this limitation, the current study integrates bidirectional long short-term memory (BiLSTM) into a three-phase framework: data preparation, model construction, and performance evaluation. In the data preparation phase, the feature vector comprises basal pressure, pressure increments, time intervals, and prior settlement values to facilitate a rolling forecast. To manage unevenly spaced data, an Akima spline standardizes the desired time intervals. The model’s efficacy is validated using observational data from two distinct construction case studies, each featuring diverse soil conditions. BiLSTM proves effective in identifying key attributes from load and settlement data during the staged construction process. Compared to traditional curve-fitting methods, the BiLSTM model exhibits superior performance, robustness, and adaptability to varying soil conditions. Additionally, the model demonstrates low sensitivity to the range of post-construction data, allowing for a data collection period reduction—from six months to three—without compromising prediction accuracy (relative error = 0.92%). These advantages not only optimize resource allocation but also contribute to broader sustainability objectives. Full article
(This article belongs to the Special Issue AI in Action: Advancing Infrastructure Inspection, Monitoring, and Management for Sustainable and Resilient Performance)
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