Search

Civil infrastructure assets' contribution to countries' economic growth is significantly increasing due to the rapid population growth and demands for public services. These civil infrastructures, including roads, bridges, railways, tunnels, dams, residential complexes, and commercial buildings, experience significant deterioration from the surrounding harsh environment. Traditional methods of visual inspection and non-destructive tests are generally undertaken to monitor and evaluate the structural health of the infrastructure. However, these methods lack reliability due to the need for instrumentation calibration and reliance on subjective visual judgments. Digital twin (DT) technology digitally replicates existing infrastructure, offering significant potential for real-time intelligent monitoring and assessment of structural health. This study reviews the existing applications of DTs across various sectors. It proposes an approach for developing DT applications in civil infrastructure, including using the Internet of Things, data acquisition, and modelling, together with the platform requirements and challenges that may be confronted during DT development. This comprehensive review is a state-of-the-art review of advancements and challenges in DT technology for intelligent monitoring and maintenance of civil infrastructure. [ABSTRACT FROM AUTHOR]

Data-driven digital transformation is becoming increasingly relevant. Building information modelling (BIM) and geographic information systems (GIS) are two technologies specific to the construction industry. The two approaches are different, but complementary. In this article, BIM–GIS integration is approached from some of the most relevant aspects, such as standardization or level of detail, and a comparison between both approaches is presented with the aim of improving the operation and maintenance of urban infrastructure. By means of the Madrid Calle 30 ring road as a case study, the integration of the BIM model of the road in a GIS scenario using the IFC and SLPK formats is shown. The information is stored in an external database, which allows updates without modifying the 3D model and facilitates the inclusion of real-time data. The study highlights the challenges of interoperability between BIM and GIS, as well as the need for open standards and software tools that enable a wider implementation in the FM of this type of infrastructure. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design. [ABSTRACT FROM AUTHOR]

Cracks are a common defect in civil infrastructures, and their occurrence is often closely related to structural loading conditions, material properties, design and construction, and other factors. Therefore, detecting and analyzing cracks in civil infrastructures can effectively determine the extent of damage, which is crucial for safe operation. In this paper, Web of Science (WOS) and Google Scholar were used as literature search tools and "crack", "civil infrastructure", and "computer vision" were selected as search terms. With the keyword "computer vision", 325 relevant documents were found in the study period from 2020 to 2024. A total of 325 documents were searched again and matched with the keywords, and 120 documents were selected for analysis and research. Based on the main research methods of the 120 documents, we classify them into three crack detection methods: fusion of traditional methods and deep learning, multimodal data fusion, and semantic image understanding. We examine the application characteristics of each method in crack detection and discuss its advantages, challenges, and future development trends. [ABSTRACT FROM AUTHOR]

Indications that corruption mitigation in infrastructure systems delivery can be effective are found in the literature. However, there is an untapped opportunity to further enhance the efficacy of existing corruption mitigation strategies by placing them explicitly within the larger context of engineering ethics, and relevant policy statements, guidelines, codes and manuals published by international organizations. An effective matching of these formal statements on ethics to infrastructure systems delivery facilitates the identification of potential corruption hotspots and thus help establish or strengthen institutional mechanisms that address corruption. This paper reviews professional codes of ethics, and relevant literature on corruption mitigation in the context of civil engineering infrastructure development, as a platform for building a structure that connects ethical tenets and the mitigation strategies. The paper assesses corruption mitigation strategies against the background of the fundamental canons of practice in civil engineering ethical codes. As such, the paper’s assessment is grounded in the civil engineer’s ethical responsibilities (to society, the profession, and peers) and principles (such as safety, health, welfare, respect, and honesty) that are common to professional codes of ethics in engineering practice. Addressing corruption in infrastructure development continues to be imperative for national economic and social development, and such exigency is underscored by the sheer scale of investments in infrastructure development in any country and the billions of dollars lost annually through corruption and fraud. [ABSTRACT FROM AUTHOR]

Civil infrastructure underpins urban receipts of food, energy, and water (FEW) produced in distant watersheds. In this study, we map flows of FEW goods from watersheds of the contiguous United States to major population centers and highlight the critical infrastructure that supports FEW flows. To do this, we draw upon detailed records of agriculture, electricity, and public water supply production and couple them with commodity flow and infrastructure information. We also compare the flows of virtual water embedded in food and energy commodity flows with physical water flows in inter‐basin water transfer projects around the country. We found that the virtual blue water transfers through crops and electricity to major US cities was 53 billion and 8 billion m3 in 2017, respectively, while physical interbasin water transfers for crops, electricity, and public supply water averaged 20.8 billion m3. Highways are the primary infrastructure used to import virtual water associated with food and fuel into cities, although waterways and railways are most utilized for long‐distance transport. All of the 204 watersheds in the contiguous US support the food, energy, and/or water supplies of major US cities, with dependencies stretching far beyond each city's borders. Still, most cities source the majority of their FEW and embedded water resources from nearby watersheds. Infrastructure such as water supply dams and inland ports serve as important buffers for both local and supply‐chain sourced water stress. These findings can inform efforts to reduce water resources and infrastructure risks in domestic supply chains. Key Points: Civil infrastructure enables US cities to access FEW resources from distant watershedsMost cities depend on nearby watersheds for FEW and embedded waterVirtual water transfers through food and energy exceed physical water transfers [ABSTRACT FROM AUTHOR]

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real‐time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self‐powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG‐enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG‐enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services. [ABSTRACT FROM AUTHOR]

Effective stormwater management can be used to regulate water quantity and quality for environmental sustainability, flood control, pollution reduction and other advantages of civil engineering infrastructures. Pollution of the environment and contamination of water sources can emanate from improper stormwater management. This study used a smallscale model of rainwater harvesting to analyze the design and model of urban stormwater management and treatment infrastructure for the neighborhoods in Abuja. The water quality of the treated stormwater retrieved has improved as a result of the usage of memory foam, alum, and chlorine to filter out contaminants and pathogens. With the fictitious stormwater treatment model created for this study, average values of the physicochemical parameters were collected from the stormwater discharge after it had been filtered and treated. The use of potash alum has had a variety of effects on the water's quality. From 697 mg.L-1 to 635 mg.L-1, the total dissolved solids dropped. The DO dropped from 5.87 mg.L-1 to 3.92 mg.L-1 as well. Additionally, the turbidity rose from 4.42 FNU to 4.58 FNU, and the salinity rose from 0.7 PSU to 1.44 PSU, respectively. pH decreases from 19.78 to 15.17 mg.L-1, BOD decreases from 8.35 to 6.51, and COD decreases from 2.55 to 1.9. Calcium hardness has decreased from 287 mg.L-1 to 265.83 mg.L-1. The conductivity increases marginally from 3.24 ms.cm-1 to 3.82 ms.cm-1. The Fe2+ and Zn2+ ions exhibit a little decrease from 0.143 mg.L-1 to 0.055 mg.L-1 and from 0.092 mg.L-1 to 0.045 mg.L-1, respectively. Due to inadequate or nonexistent drainage systems in the many states and villages throughout the country, stormwater run-off management and treatment in Nigeria have been a colossal failure. Effective stormwater management can be sustained by using legal and environmental laws. [ABSTRACT FROM AUTHOR]

Abstract Civil infrastructure underpins urban receipts of food, energy, and water (FEW) produced in distant watersheds. In this study, we map flows of FEW goods from watersheds of the contiguous United States to major population centers and highlight the critical infrastructure that supports FEW flows. To do this, we draw upon detailed records of agriculture, electricity, and public water supply production and couple them with commodity flow and infrastructure information. We also compare the flows of virtual water embedded in food and energy commodity flows with physical water flows in inter‐basin water transfer projects around the country. We found that the virtual blue water transfers through crops and electricity to major US cities was 53 billion and 8 billion m3 in 2017, respectively, while physical interbasin water transfers for crops, electricity, and public supply water averaged 20.8 billion m3. Highways are the primary infrastructure used to import virtual water associated with food and fuel into cities, although waterways and railways are most utilized for long‐distance transport. All of the 204 watersheds in the contiguous US support the food, energy, and/or water supplies of major US cities, with dependencies stretching far beyond each city's borders. Still, most cities source the majority of their FEW and embedded water resources from nearby watersheds. Infrastructure such as water supply dams and inland ports serve as important buffers for both local and supply‐chain sourced water stress. These findings can inform efforts to reduce water resources and infrastructure risks in domestic supply chains.

Data-driven digital transformation is becoming increasingly relevant. Building information modelling (BIM) and geographic information systems (GIS) are two technologies specific to the construction industry. The two approaches are different, but complementary. In this article, BIM–GIS integration is approached from some of the most relevant aspects, such as standardization or level of detail, and a comparison between both approaches is presented with the aim of improving the operation and maintenance of urban infrastructure. By means of the Madrid Calle 30 ring road as a case study, the integration of the BIM model of the road in a GIS scenario using the IFC and SLPK formats is shown. The information is stored in an external database, which allows updates without modifying the 3D model and facilitates the inclusion of real-time data. The study highlights the challenges of interoperability between BIM and GIS, as well as the need for open standards and software tools that enable a wider implementation in the FM of this type of infrastructure.

Building Information Modeling (BIM) has emerged as a transformative technology in the Architecture, Engineering, and Construction (AEC) industry, with increasing application in civil infrastructure projects. This study comprehensively reviews the research landscape of BIM applications in civil infrastructure through bibliometric analysis. Based on data from the Web of Science database, 646 relevant papers published between 2020 and 2024 were collected, and 416 papers were selected for in-depth analysis after screening. Using bibliometric methods, the analysis reveals the evolution of research trends, identifies key contributors and influential publications, and maps the knowledge structure of the field. Our study shows a significant increase in research output over the past five years, particularly in studies focusing on the integration of BIM with emerging technologies such as Digital Twins, the Internet of Things (IoT), and Machine Learning. The results indicate that the United States, China, and the United Kingdom lead in terms of research output and citation impact. Additionally, based on clustering results and representative keywords, several key research clusters were identified, including BIM in infrastructure lifecycle management, BIM collaboration in large-scale projects, and BIM for sustainable infrastructure design.

The application of FRPs in civil infrastructure has increased, particularly in the last 20 years. FRPs have gained importance because of their resistance to harsh environments, high strength-to-weight ratio, and good corrosion resistance, and they are faster and easier to apply than other traditional methods. The rehabilitation of structures is the main area in which FRPs have been developed, because they have allowed for compliance with architectural restraints in historic structures. This review is a compilation of the research conducted on the laboratory and field applications of FRPs, highlighting the different applied methods, installation difficulties, and failure modes of FRPs. Moreover, this review compares studies on the types of fibers such as CFRPs, GFRPs, and AFRPs, and their effects would affect the mechanical properties of civil infrastructure and the durability characteristics of civil infrastructure in challenging environmental conditions. In addition, this review focuses on the modification of the mechanical properties of structural elements using different methods of installing FRPs, including externally bonded reinforcement (EBR), and their main problem: debonding failure before the ultimate load. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a technology superimposes interactional virtual objects onto a real environment. Since the beginning of the millennium, AR technologies have rapidly grown, with significant research publications in engineering and science. However, the civil infrastructure community has minimally implemented AR technologies to date. Civil engineers face one of the challenges when understanding and using AR is the lack of a classification of AR in the context of capabilities for civil infrastructure applications. Like most engineering fields, practitioners in civil infrastructure prioritize understanding a new technology's maturity level before considering its adoption and field implementation. This article compares the capabilities of 16 AR head‐mounted devices (HMDs) available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations. Finally, the authors recommend a development framework for functional AR interfaces with civil infrastructure and operations. [ABSTRACT FROM AUTHOR]

Abstract Augmented reality (AR) is a technology superimposes interactional virtual objects onto a real environment. Since the beginning of the millennium, AR technologies have rapidly grown, with significant research publications in engineering and science. However, the civil infrastructure community has minimally implemented AR technologies to date. Civil engineers face one of the challenges when understanding and using AR is the lack of a classification of AR in the context of capabilities for civil infrastructure applications. Like most engineering fields, practitioners in civil infrastructure prioritize understanding a new technology's maturity level before considering its adoption and field implementation. This article compares the capabilities of 16 AR head‐mounted devices (HMDs) available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations. Finally, the authors recommend a development framework for functional AR interfaces with civil infrastructure and operations.

Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry. [ABSTRACT FROM AUTHOR]

The successful development of deep learning and computer vision techniques has recently revolutionized structural health diagnosis (SHD) during life-cycle construction, inspection, maintenance, and disaster prevention for civil infrastructure. Multi-source big data of structural health monitoring (SHM) and inspection are projected into a high-level feature space, and data-driven models are established to map the relationships between inputs and outputs and dig out embedded structural behaviors and implicit physical mechanisms. However, the model performance highly relies on the extensive amount and diversity, intra-class completeness, and inter-class balance of training data, and the generalization ability on scarce data with specific features and particular patterns is challenging under real-world scenarios. To address the above challenges, few-shot learning (FSL) has emerged as a cutting-edge machine learning technique that designs training strategies using only a small amount of annotated data under a limited supervision regime to enhance effectiveness and generalization ability. This article systematically summarizes recent advances in FSL algorithms and the corresponding applications in SHD for civil infrastructure. A unified mathematical framework of FSL is formulated, and an FSL taxonomy is summarized according to intrinsic learning mechanisms and implementation principles, including metric learning-based, optimization-based, transfer learning-based, and generative model-based methods. Various applications of SHD for civil infrastructure under real-world scenarios are reviewed, including remote sensing monitoring, structural damage recognition, post-disaster safety evaluation, and construction risk assessment. Error analyses of approximation, generalization, and optimization errors corresponding to the four paradigms mentioned above are formulated for FSL-based data-driven modeling, which exactly acts as the logical connection with how new thoughts of FSL-based SHD should be designed. Finally, potential prospects of FSL-based SHD are outlined to design weakly supervised FSL considering low-quality data from SHM systems, develop multi-modal FSL for multi-source SHM data, establish cross-task FSL with high commonality and generality for various SHD tasks, and construct lightweight FSL framework with model compression for real-world applications of SHD considering the super-large stream of monitoring data. [ABSTRACT FROM AUTHOR]

This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA. [ABSTRACT FROM AUTHOR]

Information technologies have widely been used in the construction and maintenance of civil infrastructure. The advantages of information technologies provided a broader range of methods for infrastructure and enhanced its level of maintenance. However, a systematic summary of the research development of information technologies used in civil infrastructure is limited. This study aims to supplement this field by providing an objective, systematic summary of relevant literature in mainstream journals employing bibliometric retrieval and quantitative analysis from 2010 to 2020. The following results are obtained: (1) This study discusses the application of advanced information technologies in different phases and provides a critical analysis of the application of these existing information technologies, which includes wireless sensor networks (WSN), fiber optic sensing (FOS), building information modelling (BIM), radio frequency identification (RFID) and other advanced information technologies. (2) The digital twins can be used as tools for the planning and management of next-generation smart infrastructure, making the future of civil infrastructure smarter and more sustainable. [ABSTRACT FROM AUTHOR]

This paper presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. All four communities are underlain by continuous ice-rich permafrost with varying degrees of degradation and coastal erosion. The types, locations, and periods of observed permafrost thaw and coastal erosion were elicited. Survey participants also reported the types of civil infrastructure being affected by permafrost degradation and coastal erosion and any damage to residential buildings. Most survey participants reported that coastal erosion has been occurring for a longer period than permafrost thaw. Surface water ponding, ground surface collapse, and differential ground settlement are the three types of changes in ground surface manifested by permafrost degradation that are most frequently reported by the participants, while houses are reported as the most affected type of infrastructure in the Arctic coastal communities. Wall cracking and house tilting are the most commonly reported types of residential building damage. The effects of permafrost degradation and coastal erosion on civil infrastructure vary between communities. Locations of observed permafrost degradation and coastal erosion collected from all survey participants in each community were stacked using heatmap data visualization. The heatmaps constructed using the community survey data are reasonably consistent with modeled data synthesized from the scientific literature. This study shows a useful approach to coproduce knowledge with Arctic residents to identify locations of permafrost thaw and coastal erosion at higher spatial resolution as well as the types of infrastructure damage of most concern to Arctic residents. [ABSTRACT FROM AUTHOR]

There has been a strong need for simulation environments that are capable of modeling deep interdependencies between complex systems encountered during natural hazards, such as the interactions and coupled effects between civil infrastructure systems response, human behavior, and social policies, for improved community resilience. Coupling such complex components with an integrated simulation requires continuous data exchange between different simulators simulating separate models during the entire simulation process. This can be implemented by means of distributed simulation platforms or data passing tools. In order to provide a systematic reference for simulation tool choice and facilitating the development of compatible distributed simulators for deep interdependent study in the context of natural hazards, this article focuses on generic tools suitable for integration of simulators from different fields but not the platforms that are mainly used in some specific fields. With this aim, the article provides a comprehensive review of the most commonly used generic distributed simulation platforms (Distributed Interactive Simulation (DIS), High Level Architecture (HLA), Test and Training Enabling Architecture (TENA), and Distributed Data Services (DDS)) and data passing tools (Robot Operation System (ROS) and Lightweight Communication and Marshalling (LCM)) and compares their advantages and disadvantages. Three specific limitations in existing platforms are identified from the perspective of natural hazard simulation. For mitigating the identified limitations, two platform design recommendations are provided, namely message exchange wrappers and hybrid communication, to help improve data passing capabilities in existing solutions and provide some guidance for the design of a new domain-specific distributed simulation framework. [ABSTRACT FROM AUTHOR]

Large civil infrastructure projects, such as airports, highways, railways, townships, and other developments, are critical for contributing to the social, economic, and environmental pillars of sustainability along with the UN-SDGs. However, it is not enough to simply assess their impacts; organizations and stakeholders involved in creating, operating, and maintaining these projects must also report on them. Recent reports by UNEP indicate that the civil infrastructure sector is slow in adopting these principles of sustainability assessment and reporting. The aim of this study is to develop an integrated approach for assessing the sustainability of civil infrastructure projects and sustainability reporting for organizations and stakeholders while achieving the UN-SDGs. To achieve this aim, the study employs a two-step process. In the first step, a correspondence matrix is derived from the sustainability assessment parameters of various infrastructure rating tools (InRTs), Global Reporting Initiative (GRI) standards, and UN-SDGs targets. In the second step, a composite indicator called the Sustainability Alignment Index (SAI) is developed. The SAI comprises two sub-indices, namely the TBL-Sustainability Coverage Index (TSCI) and the TBL-Sustainability Disclosure Quality Index (TSDQI). The correspondence matrix, indices, and sub-indices are then used to evaluate the quantitative and qualitative aspects of the sustainability disclosures of selected civil infrastructure companies in Asia and Europe. The study's findings question the comprehensiveness of the evaluating criteria used in InRTs. It also reveals the lack of alignment between an infrastructure project's sustainability assessment criteria, GRI standards, and UN-SDGs targets. Moreover, the results emphasize the insufficient coverage of environmental sustainability assessment and reporting parameters in the disclosures made by the companies. The developed correspondence matrix, indices, and sub-indices can be used by organizations for comparing both quantitative and qualitative measures to understand their relative positions. Additionally, it will enable the public, policymakers, and other key stakeholders to compare different dimensions of sustainability more effectively. [ABSTRACT FROM AUTHOR]

The 4th industrial revolution started in 2016 and referred to a new phase in the industrial revolution. One of the most significant technological evolvements during the 4th industrial revolution is Augmented Reality (AR) technology. AR superimposes interactional virtual objects/images to real environments. Because of the interaction and see-through characteristics, AR is better applied to engineering than Virtual Reality (VR). The application of AR in civil infrastructure can avoid artificial mistakes, improve efficiency, and saves budget. This article reviews AR applications in civil infrastructure, focusing on research studies in the latest five years (2016–2020) and their milestone developments. More than half of the AR research and implementation studies have focused on the construction domain in the last five years. Researchers deploy AR technologies in on-site construction to assist in discrepancy checking, collaborative communication, and safety checking. AR also uses building information models (BIMs) to produce detailed 3D structural information for visualization. Additionally, AR has been studied for structural health monitoring (SHM), routine and damage detection, energy performance assessment, crack inspection, excavation, and underground utility maintenance. Finally, AR has also been applied for architecture design, city plan, and disaster prediction as an essential part of smart city service. This article discusses the challenges of AR implementation in civil infrastructure and recommends future applications.

Civil infrastructure systems (CIS) require effective systems-level operation and maintenance (O&M) processes to ensure safety and efficiency. Such processes demand significant human efforts in human/team cognition, decision-making, and execution of activities. Poor human behaviors could affect CIS O&M safety and efficiency. This review synthesized human reliability issues on three aspects – 1) Human-Physical, 2) Human-Human, and 3) Human-Cyber reliabilities, and thereby revealed research gaps to guide the development of methods that could achieve guaranteed CIS O&M safety and efficiency. One challenge is the lack of quantitative representations to formalize spatiotemporal, engineering process, and team behavioral models that quantify the impacts of various human factors on CIS O&M safety and efficiency. Besides, limited human/team behavioral data are available yet for comprehending human factors in highly uncertain CIS O&M scenarios. The paper concludes with future directions to facilitate multidisciplinary discussions to tackle the identified challenges.

This paper presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. All four communities are underlain by continuous ice-rich permafrost with varying degrees of degradation and coastal erosion. The types, locations, and periods of observed permafrost thaw and coastal erosion were elicited. Survey participants also reported the types of civil infrastructure being affected by permafrost degradation and coastal erosion and any damage to residential buildings. Most survey participants reported that coastal erosion has been occurring for a longer period than permafrost thaw. Surface water ponding, ground surface collapse, and differential ground settlement are the three types of changes in ground surface manifested by permafrost degradation that are most frequently reported by the participants, while houses are reported as the most affected type of infrastructure in the Arctic coastal communities. Wall cracking and house tilting are the most commonly reported types of residential building damage. The effects of permafrost degradation and coastal erosion on civil infrastructure vary between communities. Locations of observed permafrost degradation and coastal erosion collected from all survey participants in each community were stacked using heatmap data visualization. The heatmaps constructed using the community survey data are reasonably consistent with modeled data synthesized from the scientific literature. This study shows a useful approach to coproduce knowledge with Arctic residents to identify locations of permafrost thaw and coastal erosion at higher spatial resolution as well as the types of infrastructure damage of most concern to Arctic residents.

In an attempt to encourage and incentivise sustainable infrastructure practices, rating tools have been developed in different geographies. This paper explores the value of such tools in educating the engineering profession and shifting action within the infrastructure industry. The paper draws on a substantive international scale research programme, which examined multiple aspects of rating tool design and use, the context in which the tools operate, and the links between the tools and an evolving sustainability agenda. Research data from 119 civil infrastructure projects and 63 interviews with infrastructure owners and tool users provided significant findings on sustainability outcomes achieved, factors affecting outcomes and the wider influence of the tools. Rating tool influence was evident beyond rated projects in practitioner's broader professional practice, in policies and practices within their home organisations and in the engineering profession and infrastructure sector more broadly. Research outputs include a conceptual framework on rating tool influence and a sustainability maturity model for infrastructure owner organisations. These outputs can assist asset owners, advisors and other professionals to develop strategies for addressing the challenges of sustainability in civil infrastructure development. In particular, the findings contextualise the tools as 'processes of change' at the individual, organisational and industry level. [ABSTRACT FROM AUTHOR]

This paper presents an extract-transform-load (ETL) approach based on multilayer task execution for processing massive sequential data collected from infrastructure operation and maintenance. The proposed approach consists of ETL task partition, execution mode selection, and ETL modeling. The task partition focuses on dividing the ETL process into four tasks to be executed in accordance with different organizational forms of data. Sequenced or non-sequenced load mode is optional, which is independent of the data standardization. In addition, the ETL modeling phase implements conceptual, logical, and physical modeling for the multi-dimensional model. Our main objective is to integrate massive sequential data, enhancing decision-making performance for the intelligent management platform. Traffic data for two years were collected from various systems and acquisition tools of different providers to evaluate the data integration capability of the proposed approach. Furthermore, Kettle software was used to perform transformation and job modules for the multilayer tasks. In addition, a machine learning algorithm was used to generate traffic warning in the tunnels based on the integrated data. The proposed approach is promising for the management and analysis of massive sequential data generated in operation, the maintenance of transportation tunnels, and effective decision-making.

Real-time health monitoring of civil infrastructures is performed to maintain their structural integrity, sustainability, and serviceability for a longer time. With smart electronics and packaging technology, large amounts of complex monitoring data are generated, requiring sophisticated artificial intelligence (AI) techniques for their processing. With the advancement of technology, more complex AI models have been applied, from simple models to sophisticated deep learning (DL) models, for structural health monitoring (SHM). In this article, a comprehensive review is performed, primarily on the applications of AI models for SHM to maintain the sustainability of diverse civil infrastructures. Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms easier. UAV is found to be the most promising smart data acquisition technology, whereas convolution neural networks are the most impressive DL model reported for SHM. Furthermore, current challenges and future perspectives of AI-based SHM systems are also described separately. Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems. The integration of SHM with IoT and cloud-based computing is leading us towards the evolution of future smart cities.

Sustainability is very quickly becoming a fundamental requirement of the construction industry as it delivers its projects; whether buildings or infrastructures. Throughout more than two decades, a plethora of modeling schemes, evaluation tools and rating systems have been introduced en route to realizing sustainable construction. Many of these, however, lack consensus on evaluation criteria, a robust scientific model that captures the logic behind their sustainability performance evaluation, and therefore experience discrepancies between rated results and actual performance. Moreover, very few of the evaluation tools available satisfactorily address infrastructure projects. This paper introduces a systems model that abstracts the environment, the construction product, and its production system as three interacting systems that basically exchange materials, energy and information. The model utilizes this setup to capture and quantify essential flows exchanged between such three systems, with the objective of evaluating sustainability. The paper walks through the development of a generic case of the model, and then demonstrates its utility in evaluating the sustainability performance of civil infrastructure projects using a typical water pipeline installation project that uses horizontal directional drilling (HDD) technology as a trenchless installation method. The developed model addresses an identified gap within the current body of knowledge by considering infrastructure projects. Through the ability to simulate different scenarios, the model enables identifying which activities, products, and processes impact the environment more, and hence potential areas for optimization and improvement.

Abstract Fibre reinforced polymer (FRP) composites have become known for their specific advantages for civil infrastructure construction. High corrosion resistance in particular is one such strength, which has led to successful applications worldwide. This paper focuses on the structural applications of FRP composites as major load-carrying members (therefore strengthening of existing structures is not included) in aggressive environments. It is review and comparatively studied for environmental effects on FRP composites at the structural level mainly about joints and connections, including elevated environmental temperatures, humidity and water immersion, and ultraviolet (UV) exposure. The quantifications of such environmental effects on structural responses further assist development of large scale civil structures made from FRP composites. We therefore also present a few implementations of FRP composites in civil infrastructure construction including i) FRP truss and frame structures in high humility areas, ii) FRP composite bumper systems for bridge piers, iii) floating FRP structures for solar panels, iv) FRP steel composite piles for foundation applications, and v) FRP sheets, planks and piles for modular assembly of a retaining wall. These results are introduced as initiatives from own experiences, with the aim of demonstrating their applicability and providing examples for others with similar needs. [ABSTRACT FROM AUTHOR]

Abstract Fatigue cracks are of critical structural safety concern in civil infrastructure. Many existing fatigue crack sensing methods are contact-based, hence extensive human operation is necessary for sensor and/or actuator deployment. In this study, we propose a vision-based non-contact approach to detect fatigue cracks through image overlapping. We treat crack breathing behavior, the small cyclic movement of the crack perpendicular to the crack path under repetitive fatigue loads, as a robust indicator for crack identification. The differential image features provoked by a breathing crack can be extracted, enhanced, and visualized through a series of image processing techniques. The performance of the proposed approach is experimentally validated through two laboratory setups including a small-scale steel compact specimen and a large-scale bridge to cross-frame connection specimen. Test results demonstrate the capability of the proposed approach in reliably identifying the fatigue crack, even the true crack is surrounded by other non-crack features. Highlights • Fatigue cracks are detected by images collected from a hand-held consumer-grade digital camera. • The technique can sense the tiny crack breathing through image overlapping. • The technique can visually distinguish true crack and non-crack features. • This method expands the applications of computer vision for fatigue crack inspection in civil infrastructure. [ABSTRACT FROM AUTHOR]

This paper describes the development of an innovative carbon nanotube-based non-woven composite sensor that can be tailored for strain sensing properties and potentially offers a reliable and cost-effective sensing option for structural health monitoring (SHM). This novel strain sensor is fabricated using a readily scalable process of coating Carbon nanotubes (CNT) onto a nonwoven carrier fabric to form an electrically-isotropic conductive network. Epoxy is then infused into the CNT-modified fabric to form a free-standing nanocomposite strain sensor. By measuring the changes in the electrical properties of the sensing composite the deformation can be measured in real-time. The sensors are repeatable and linear up to 0.4% strain. Highest elastic strain gage factors of 1.9 and 4.0 have been achieved in the longitudinal and transverse direction, respectively. Although the longitudinal gage factor of the newly formed nanocomposite sensor is close to some metallic foil strain gages, the proposed sensing methodology offers spatial coverage, manufacturing customizability, distributed sensing capability as well as transverse sensitivity.

Recent developments in sensing and monitoring techniques have led to the generation of high-dimensional data in the field of civil engineering. High-dimensional data analytics methods have thus been developed to interpret such complex data. Among the different high-dimensional data analytics techniques, matrix and tensor decomposition methods have acquired a notable interest in the civil engineering community over the past decade. Due to their unique ability to deal with highly redundant and correlated data, these methods are establishing themselves as promising and efficient tools to analyze high-dimensional data in the civil engineering arena. In this paper, high-dimensional data is referred to as a data set in which the number of features is comparable or larger than the number of observations. This review paper aims to summarize the applications of matrix and tensor decomposition methods in civil engineering over the last decade. The survey begins with a general overview of matrix and tensor decomposition followed by highlighting their significance in the field. Afterward, various applications of these high-dimensional data analytics methods in civil engineering are presented, while the advantages offered by these methods are discussed. Finally, challenges and potential research avenues for employing matrix and tensor decomposition and future emerging trends for their novel use are highlighted. • A summary of applications of matrix and tensor decomposition in civil engineering is presented. • Significance and the advantages of employing these high-dimensional data analytic methods are discussed. • Challenges and future research avenues for using matrix and tensor decomposition in civil engineering are presented. [ABSTRACT FROM AUTHOR]

Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their high cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. This research focuses on a comprehensive strategy using a mobile LiDAR mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The processing strategy delivers a set of global and local performance measure for MSE walls. Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls.

Life cycle cost (LCC) analysis offered a useful framework to think about the efficiency of infrastructures investment. However, the widely accepted algorithm of the LCC doesn’t account for the decision maker's attitude toward the risk of future consequences. The cumulative prospect theory (CPT) can be used to reveal the decision maker's preference under different risk and uncertainty conditions, and has been applied in literature to some design optimization issues. Nevertheless, previous research didn’t consider the fact that people's risk preference may vary with time, i.e., there is a reasonable tendency that people will be more risk-averse to the near future losses than to the far future losses. Recognizing this, this paper adopts the CPT model in the minimum expected cost analysis, and moreover incorporates the time-varying weighting function included in the CPT model to reflect the possible changing of risk preference toward future events. A seismic retrofit optimization of a dam built in strong earthquake-prone area is used to demonstrate the application of the method, and it is found that considering the time-varying of risk preference leads to a low design level compared with that by the CPT model, but a high design level compared with the traditional LCC. [ABSTRACT FROM AUTHOR]

Sustainability is very quickly becoming a fundamental requirement of the construction industry as it delivers its projects; whether buildings or infrastructures. Throughout more than two decades, a plethora of modeling schemes, evaluation tools and rating systems have been introduced en route to realizing sustainable construction. Many of these, however, lack consensus on evaluation criteria, a robust scientific model that captures the logic behind their sustainability performance evaluation, and therefore experience discrepancies between rated results and actual performance. Moreover, very few of the evaluation tools available satisfactorily address infrastructure projects. This paper introduces a systems model that abstracts the environment, the construction product, and its production system as three interacting systems that basically exchange materials, energy and information. The model utilizes this setup to capture and quantify essential flows exchanged between such three systems, with the objective of evaluating sustainability. The paper walks through the development of a generic case of the model, and then demonstrates its utility in evaluating the sustainability performance of civil infrastructure projects using a typical water pipeline installation project that uses horizontal directional drilling (HDD) technology as a trenchless installation method. The developed model addresses an identified gap within the current body of knowledge by considering infrastructure projects. Through the ability to simulate different scenarios, the model enables identifying which activities, products, and processes impact the environment more, and hence potential areas for optimization and improvement. [ABSTRACT FROM AUTHOR]

Automatic pavement crack segmentation plays a critical role in the field of defect inspection. Although recent segmentation-based CNNs studies showed a promising pavement crack segmentation performance, a challenging task is still remaining due to the inhomogeneity of crack intensity, the complexity of pavement environments, a limited number of labeled training datasets, and the presence of noise in the majority of images in the crack dataset, which makes it difficult to distinguish the cracks from the noise. To overcome these challenges, this study aims to exploit the inherent relationship between classification and segmentation tasks to improve pavement crack segmentation. A hybrid deep learning pavement crack semantic segmentation is proposed based on knowledge transfer among the Class Activation Maps (KTCAM) and the encoder–decoder segmentation network (KTCAM-Net) via the capability of the revised CAM strategy. An adopted trained pavement crack classification network is used to produce high-quality crack localization maps. These localization abilities are fused with the encoder's image features and fed into the decoder network to provide an accurate pavement crack segmentation. Furthermore, a hybrid loss function is developed to optimize the segmentation network, capture information about thin cracks, and tackle the problem of class imbalance. Afterward, a novel refinement process is applied to purify the segmented crack boundaries. A comprehensive experimental comparison is conducting using four benchmark datasets: DeepCrack, Crack500, CFD, and CrackSC. The proposed KTCAM-Net demonstrates the state-of-the-art segmentation results. [Display omitted] • A hybrid deep learning pavement crack semantic segmentation. • A revised CAM module is introduced to refine the CAM and improve its accuracy. • An aggregation layer is proposed to concatenate the refined CAM with encoder's features. • A hybrid loss function is developed to tackle the problem of class imbalance. • A crack boundary refinement module is proposed to optimize crack boundaries. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) composites are considered viable alternatives due to their inherent advantages over traditional materials. Their advantages include light weight, high specific strength, high durability, corrosion resistance, chemical and environmental resistance, and low maintenance cost. This innovative material has been globally applied into civil infrastructure. In Australia, the applications include composite pile system and rehabilitation, marine structures, bridge structures and electrical cross-arms. On the other hand, strengthening of bridge and building structures are the common application of fiber composites in the Philippines. This paper presents the recent developments on the application of FRP composites in Australia and in the Philippines. Challenges in the implementation of this material are also presented. It is hoped that with the information revealed in this study, it will gain further application of this innovative material into civil infrastructure here in the Philippines.

Application studies on wireless sensor networks (WSN) are actively conducted in the construction industry. However, there are several technical limitations including signal interference caused by the characteristics of wireless sensors, reliability degradation in wireless communication and uncertainty of configuring a network topology. This may lead to a decline in reliability and performance of real-time data acquisition methods. Thus, the paper developed a model capable of predicting reliability performance of wireless signals applied to civil infrastructures. The measured and pre­dicted values of wireless signals are compared and analyzed through a field experiment carried out in an actual bridge to verify the prediction model suggested herein. As a result of the analysis, the prediction model demonstrated a variation up to 8.4% compared with actual measurements, proving the high accuracy of the prediction model. Furthermore, the reception rate at short distances within a 5 m radius is at least 90%, showing a highly reliable reception capacity. When this is applied to monitoring systems in the construction sector, it is believed that performance and reliability of such system can be secured.