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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]

Civil infrastructure is continuously subject to aging and deterioration due to multiple factors, which lead to a decline in performance and impact structural health. Accumulated damage on structures increases operational costs and poses significant risks to public safety. Effective maintenance, repair, and rehabilitation strategies are needed to ensure civil infrastructure's overall safety and reliability. Non-Destructive Evaluation (NDE) methods are utilized to assess latent damage and provide decision-makers with real-time information for mitigating hazards. Within the last decade, there has been a significant increase in the research and development of innovative NDE techniques to improve data processing and promote efficient and accurate infrastructure assessment. This paper aims to review one of those methods, namely, Infrared Thermography (IRT), and its applications in civil infrastructure. A comprehensive review is presented by investigating numerous journal articles, research papers, and technical reports describing numerous IRT applications for bridges, buildings, and general civil structures made from different materials. The capability of IRT to identify and pinpoint anomalies, typically in the early stages of degradation, has excellent potential to improve the safety and shore up the dependability of civil infrastructures while reducing expenses tied to maintenance and rehabilitation. Furthermore, the non-invasive nature of IRT is beneficial in mitigating disturbances and downtime that may occur during various inspection procedures. It is highlighted that IRT is a highly versatile and effective tool for infrastructure condition assessment. With further advancement and fine-tuning of the available techniques, it is likely that IRT will continue to gain significant popularity in maintaining and monitoring civil 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]

The structural health of civil infrastructure can be monitored using non‐destructive testing methods and wireless sensor technology. Buildings are often made of reinforced concrete, in which the sensors can be embedded during construction. The implementation of sensor‐based monitoring therefore fundamentally requires robust electronics. The wireless sensors must meet the special requirements of different areas such as communication, durability in the concrete, and compatibility with future readers. Taking into account previous approaches from practice and research, an elicitation of basic requirements for a Radio Frequency Identification (RFID) sensor system that can be embedded in concrete was carried out. Specific problems related to embedding electronic sensors in concrete and their design are investigated. Simple RFID sensor systems have been developed that allow operation without cables or batteries. The main sensor requirements are listed accordingly. Challenges and issues that need to be considered when using RFID sensors in concrete are described. Finally, the "life cycle" of a sensor system embedded in reinforced concrete is briefly discussed. [ABSTRACT FROM AUTHOR]

Realistic risk assessment of civil infrastructure subjected to catastrophic events plays a crucial role in strategic asset management. In this process, visualization of the asset and its environment always possesses some epistemic uncertainty associated with the modeler's simplification/abstraction. The present paper aims at providing a critical perspective into the characterization of the seismic excitation model for risk assessment of civil infrastructures and identifying gaps in the present knowledge. For this purpose, the main factors that influence the development of a comprehensive framework for the simulation of seismic excitation are highlighted. The coupling effect of the spatial variability of ground motions and the substructure's footprint on the realization of the seismic excitation model is illustrated. Finally, the necessity of direct incorporation of the load analysis subtask in the probabilistic seismic risk assessment of civil infrastructures is discussed. In addition, simplified probability density functions are proposed for the simulation of the seismic excitation model. [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.

Nowadays, the number of aging civil infrastructures is growing world-wide and when concrete is involved, cracking and delamination can occur. Therefore, ensuring the safety and serviceability of existing civil infrastructure and preventing an inadequate level of damage have become some of the major issues in civil engineering field. Routine inspections and maintenance are then required to avoid leaving these defects unexplored and untreated. However, due to the limitations of on-field inspection resources and budget management efficiency, automation technology is needed to develop more effective and pervasive inspection processes. This paper presents a pixel-wise classification method to automatically detect and quantify concrete defects from images through semantic segmentation network. The proposed model uses Deeplabv3+ network with weights initialized from pre-trained neural networks. The comparison study among the performance of different deep neural network models resulted in ResNet-50 as the most suitable network for applications of civil infrastructure defects segmentation. A total of 1250 images have been collected from the Internet, on-field bridge inspections and Google Street View in order to build an invariant network for different resolutions, image qualities and backgrounds. A randomized data augmentation allowed to double the database and assign 2000 images for training and 500 images for validation. The experimental results show global accuracies for training and validation of 93.42% and 91.04%, respectively. The promising results highlighted the suitability of the model to be integrated in digitalized management system to increase the productivity of management agencies involved in civil infrastructure inspections and digital transformation. [ABSTRACT FROM AUTHOR]

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]

Sensor‐integrated digital twins for wireless structural health monitoring of civil infrastructure Digital twins constitute a promising technology that lies at the core of emerging trends within Industry 4.0, such as cyber‐physical systems and the Internet‐of‐Everything. The civil engineering community has been showing increasing interest in adopting digital twins for operation and management purposes, e. g. within the framework of structural health monitoring (SHM). In particular, SHM stands to benefit from the enhanced predictive capabilities of digital twins, which yield richer information on structural conditions than conventional models. However, adapting state‐of‐the‐art digital twins to modern SHM strategies, which rely on wireless technologies, is hardly straightforward. Specifically, digital twins that model a physical process are usually centralized and updated using large amounts of data collected from the physical process. By contrast, wireless SHM systems are distributed, and wireless SHM strategies typically focus on minimizing wireless communication, which is unreliable and power‐consuming. In this context, this paper presents the results of a feasibility study that discusses the practical considerations of embedding digital twins in wireless SHM systems. Through field tests on a pedestrian bridge, instrumented with a wireless SHM system, the decentralized embedment of a digital twin – in the form of partial digital twins – into the wireless sensor nodes is reported. The field test results indicate that decentralized digital twins can advantageously be used for advancing wireless SHM. [ABSTRACT FROM AUTHOR]

Landscape design of major civil infrastructure works has often been undermined as a policy requirement or been neglected in practice. We investigate whether this is justified by technical challenges, high costs or proven lack of utility of landscape design of infrastructure, focussing on dam-design practice. Initially, we investigate global practice and identify 56 cases of dams in which landscape or architectural treatment has been applied. We then create a typology of utilised design techniques and investigate their contribution to improving landscape quality perception through literature review and through the analysis of photograph upload densities in geotagged photography databases. Finally, we investigate costs of landscape works, analysing three dam projects in detail. The results demonstrate that landscape design of civil infrastructure (a) improves landscape quality perception of infrastructures' landscapes and (b) that its implementation can be both economically and technically feasible, especially if existing knowledge from best practices is utilised. [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 is a critical shortage in research needed to explore a new class of multifunctional structural components that respond to their environment, empower themselves and self‐monitor their condition. Here, the novel concept of triboelectric nanogenerator‐enabled structural elements (TENG‐SEs) is proposed to build the foundation for the next generation civil infrastructure systems with intrinsic sensing and energy harvesting functionalities. In order to validate the proposed concept, proof‐of‐concept multifunctional composite rebars with built‐in TENG mechanisms are developed. The developed prototypes function as structural reinforcements, nanogenerators, and distributed sensing mediums under external mechanical vibrations. Experiential and theoretical studies are performed to verify the electrical and mechanical performance of the developed self‐powering and self‐sensing composite structural components. The capability of the embedded structural elements to detect damage patterns in concrete beams at multiscale is demonstrated. Finally, it is discussed how this new class of TENG‐SEs can revolutionize the large‐scale distributed monitoring practices in civil infrastructure and construction fields. [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.

Deflection measurements on structures continue to be a challenge with current sensor technologies. Material degradation and changes in the mechanical properties over time (e.g. creep and shrinkage in concrete bridges) directly impact the deflections exhibited by a structure. In this article, we introduce and discuss the evaluation of a novel laser- and video-based displacement sensor prototype to monitor displacements and rotations on structures remotely. The sensor is inexpensive, using off-the shelf components, but also accurate and practical for situations that do not allow the use of conventional displacement sensors, which require a reference base. In contrast to other image-based approaches such as digital image correlation (DIC) or Eulerian-based virtual video sensors (VVS), the digital camera of our proposed solution is located at the measurement location on the structure. The sensor was evaluated using laboratory tests to determine the practicality, accuracy, and sensitivity to lighting conditions. The accuracy of the sensor was found to be approximately ± 0.9 mm (± 0.035 in) (95% prediction limits) for a 30.5 m (100 ft) measurement distance under laboratory conditions. Finally, we applied and evaluated the sensor under real-world conditions on a concrete deck/single steel box girder pedestrian bridge under static and dynamic loading conditions as well as on a five-story steel moment-frame building under ambient conditions. Essential for field applications, the results demonstrate the prototype offers an inexpensive yet practical and accurate solution for monitoring displacements and rotations remotely. [ABSTRACT FROM AUTHOR]

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.