Civil Infrastructure Systems Research Articles

Life cycle sustainability assessments of critical civil infrastructure systems: additional guidance for future research and practical application

ABSTRACT Sustainable development of critical civil infrastructure systems should include economic, environmental, and social considerations, as well as considerations that are of high value to stakeholders and decision-makers. Life cycle economic and environmental impacts of critical civil infrastructure systems are commonly quantified by well-established methodologies such as life cycle assessment and life cycle costing. However, social and other related impacts of critical civil infrastructure systems can be difficult to quantify and are therefore less commonly and thoroughly addressed. A mini-literature review was conducted to explore how sustainability is currently evaluated for critical civil infrastructure systems. The review found that, while there are life cycle assessment frameworks available, there is vague guidance available for addressing factors that are not directly related to environmental, economic, or social (i.e., difficult to quantify) impacts. In response, the authors build upon currently available methodologies by discussing a supplemental approach for collecting additional data for critical civil infrastructure systems, which includes the use of stakeholder discussions and semistructured interviews. Including a range of factors early on in life cycle sustainability assessments of critical civil infrastructure can improve study robustness and allow for further integration of decision-making factors in life cycle sustainability assessments.

Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.

Fusing infrastructure health monitoring data in point cloud

This paper presents a unique approach to data fusion in the condition assessment of civil infrastructure systems. The approach fuses dynamic monitoring sensor data with the visual data collected from a bridge structure. Such a data fusion method, named ModeShapeFuser, enables the visualization of mode shapes of engineering structures in their point cloud, offering a higher spatial resolution model of the dynamic behaviors of existing structures. A bridge case study shows that utilizing this immersive data visualization technique can potentially provide a more intuitive and holistic understanding of the dynamic behavior characteristics of engineering structures. The outputs of ModeShapeFuser, the high spatial resolution models of the bridge, are also presented in a Virtual Reality (VR) environment through a head-mounted display and a VRTable, providing a virtual tour of the bridge in its real environment. The study presented herein is critical for an informed decision-making process in assessing infrastructure systems.

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.

Mapping Permafrost Variability and Degradation Using Seismic Surface Waves, Electrical Resistivity, and Temperature Sensing: A Case Study in Arctic Alaska

AbstractSubsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiaġvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (Vs) between 240 and 370 m/s, and permafrost Vs between 450 and 1,700 m/s, typically showing a low‐high‐low velocity pattern. Additionally, we find an inverse relationship between in situ Vs and ground temperature measurements. The Vs profiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice‐rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering the Vs, and reducing heterogeneity. We show how the resulting Vs profiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions.

Nonresilience curve: A new tool for use in resilience assessment

AbstractResilience assessment is a widely used method to evaluate the ability of an object (e.g., an individual structure, or a system consisting of multiple interacting structures) to withstand, recover from, and adapt to disruptive events. This paper proposes a novel concept of “nonresilience curve,” which measures the nonresilience (complement of resilience) of an object of interest conditional on a specific hazard intensity. It is by nature an extension of the well‐established fragility curves, integrating the multiple damage states of a posthazard object. The applicability of the proposed nonresilience curve to individual structures and systems (including series systems, parallel systems, and more general and complicated systems) has been demonstrated in this paper. It is also preliminarily shown that the shape of the cumulative distribution function of a lognormal distribution is suitable to approximate the nonresilience curve, if only limited data points associated with the target nonresilience curve are available. Since the nonresilience curve is a function of the hazard intensity measure (IM), one can estimate the nonresilience of an object in a fully probabilistic manner by additionally taking into account the uncertainty associated with the IM. The proposed nonresilience curve can be further extended to formulate nonresilience surface, which is a joint function of both the IM and the available resource that supports the posthazard recovery process. The nonresilience curve is promising to be adopted in engineering practice for resilience assessment and resilience‐based design of civil structures and infrastructure systems.

A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan

ABSTRACT Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Q o ; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, R t . This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Q o , R t , and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.

Experimental Studies and Application of Fiber-Reinforced Polymers (FRPs) in Civil Infrastructure Systems: A State-of-the-Art Review.

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.

Life Cycle Costing for Structural Analysis and Design

As the construction sector is a major contributor to the environmental pressures and impacts that are accelerating the climate crisis, there is an increasing focus on sustainability, adoption of innovative technologies and materials. The European Commission (EC) already in 2019 with the Green Deal recommended also in the construction sector the use of life cycle approaches to promote materials and strategies aimed at the transition to the circular economy (CE).In this context, Life Cycle Costing (LCC) is one of the successful techniques to identify the total cost of construction goods. Life cycle engineering has emerged as an effective approach to improve product and material design, to reduce maintenance and to contain whole life costs. Therefore, it is increasingly necessary to use both technical and economic evaluation approaches when selecting investments in order to contain the overall costs for the decision-maker until the project is decommissioned. Although this technique has around 50 years of history, its application and diffusion in the construction sector is still relatively limited.This paper first provides an overview of existing research on the Life Cycle-based approach for the economic evaluation of new structural materials and civil infrastructure systems. Then, LCC-based methodology is defined to assess and compare the structural and economic performance of civil engineering constructions. The logical-operational steps on which the analysis approach is based concern: (i) structural analysis of buildings; (ii) estimation of life-cycle costs; (iii) evaluation of the ‘Global Cost’ economic indicator; (iv) comparison of the economic and structural performance of design alternatives. This approach allows the designer to choose the material with the lowest life-cycle cost for the same mechanical and technical characteristics. In general terms, the use of such a methodology enables effective investment choices and can be a valuable support in the field of civil engineering whenever it is necessary to identify the most cost-effective of several structural alternatives.Research prospects concern the validation of the model through its application to case studies. It is also envisaged extending the study to the assessment of the environmental and social impacts arising from design alternatives.

Application of Italian Guidelines for structural-foundational and seismic risk classification of bridges: the Fabre experience on a large bridge inventory

The recent issuance of Guidelines for the classification and management of risk, safety assessment, and monitoring of existing bridges has standardized the methodology for analyzing the safety and managing bridges at a national level. The Guidelines propose a multi-level analysis approach, where the assessment of structures is conducted with increasing levels of detail and complexity. This paper describes the work carried out by the Fabre Consortium, the Italian scientific alliance on risk assessment and monitoring of civil infrastructural systems, together with ANAS s.p.a., one of the major Italian road authorities, for the implementation of the Italian Guidelines to a large bridge inventory distributed over the Italian territory. This paper is specifically devoted to structural-foundational and seismic risk classification. The results of the application of the methodology to a large database is presented together with statistical analyses on parameters determining hazard, vulnerability and exposure and with the definition of the most recurrent typological bridge classes within the bridge inventory.

Failure Probability Prediction for Offshore Floating Structures Using Machine Learning

Summary Accurately estimating the failure probability is crucial in designing civil infrastructure systems, such as floating offshore platforms for oil and gas processing/production, to ensure their safe operation throughout their service periods. However, as a system becomes complex, the evaluation of a limit state function may involve the use of an external computer solver, resulting in a significant computational burden to perform Monte Carlo simulations (MCS). Moreover, the high-dimensionality of the limit state function may limit efficient sampling of input variables due to the “curse of dimensionality.” To address these issues, an efficient machine learning framework is proposed, combining polynomial chaos expansion (PCE) and active subspace. This will enable the accurate and efficient evaluation of the failure probability of an offshore structure, which typically involves a large number of uncertain parameters. Unlike conventional PCE schemes that use the original random variable space or the auxiliary variable space for building a surrogate model, the proposed method utilizes a reduced-dimension space to circumvent the “curse of dimensionality.” An appropriate coordinate transformation is first sought so that most of the variability of a limit state function can be accounted for. Next, a PCE surrogate limit state function is constructed on the derived low-dimensional “active subspace.” The Gram-Schmidt orthogonalization process is used for making basis polynomial functions, which is particularly effective when input random parameters do not follow the Askey scheme and/or when a dependence structure between the input parameters exists. Therefore, a nonlinear iso-probabilistic transformation, which makes the convergence of a surrogate to the true model difficult, is not required, unlike traditional PCE. Numerical examples, including limit state functions related to structural dynamics problems, are presented to illustrate the advantages of the proposed method in estimating failure probabilities for complex structural systems. Specifically, the method exhibits significantly improved efficiency in estimating the failure probability of an offshore floating structure without compromising accuracy as compared to traditional PCE and MCS.

Moisture and Glass Transition Temperature Kinetics of Ambient-Cured Carbon/Epoxy Composites

Carbon fiber reinforced polymer composites are widely used in the rehabilitation, repair, and strengthening of civil, marine, and naval infrastructure and structural systems. In these applications, they are exposed to a range of exposure conditions, including humidity and immersion, which are known to affect the durability of the resin and the fiber–matrix interface over long periods of time. This paper presents results of long-term hygrothermal aging of wet layup carbon/epoxy composites including through acceleration by temperature focusing on the development of a comprehensive understanding of moisture uptake kinetics and its effects on glass transition temperature and interface and inter-/intra-laminar dominated performance characteristics. A two-phase model for uptake that incorporates both diffusion- and relaxation-/deterioration-dominated regimes, as well as a transition regime, is shown to describe uptake well. The inclusion of damage terms to the diffusion and relaxation coefficients is seen to capture changes well, with the effective diffusion and relaxation coefficients increasing with fiber volume fraction and temperature. Effects of uptake, including at elevated temperatures, reflective of accelerated aging, on glass transition temperature and flexural strength are correlated, emphasizing a three-stage progression of overall response in line with the moisture uptake changes. The drop in glass transition temperature per percent increase in moisture uptake was seen to range from a low of 4.38% per % increase in moisture content, for the highest volume fraction at the highest temperature, to a high of 6.95% per % increase in moisture content, for the intermediate volume fraction at the lowest temperature. The composites with heavier fabric showed the greatest drop in both glass transition temperature and flexural strength, indicating a level of correlation between these characteristics as well. It is shown that both glass transition temperature and flexural strength show steep initial decreases, followed by a regime with slower decrease and, then, an asymptotic or near-asymptotic response with time of immersion, suggesting a close correlation with moisture uptake, which forms the basis for future modeling.

Spatial and temporal evolution of post-disaster data for damage assessment of civil infrastructure systems

Assessing damage to civil infrastructure is a resource-intensive process that is critical during the response to a disaster. Various datasets facilitate this process but are often collected on an individual ad hoc basis by multiple separate entities. Consequently, there is a lack of a coordinated approach when collecting disaster data, which prevents effective data interoperability. Rather than viewing datasets individually, this paper provides a comprehensive analysis of post-disaster damage data to demonstrate the merits of a dynamic data collection process accounting for both spatial and temporal variations. Specifically, datasets from Hurricane Maria and the Indios Earthquake in Puerto Rico are used to illustrate the entities involved, resources used, and resulting datasets for this purpose. The paper analyzes the evolution of key metadata features as a function of time, including data availability, coverage, and resolution. The results show distinct stages of the data collection process and reveal challenges in collaboration between entities and a lack of data integration for disaster response. The findings also lead to recommendations about the essential metadata for increased shareability. With these outcomes, entities in the field can improve the quality of information extracted and facilitate interoperability and information integration across datasets for damage assessment.

An Infrastructure Management Humanistic Approach for Smart Cities Development, Evolution, and Sustainability

Over the next decades, people will continue moving to urban areas all over the world, increasing infrastructure needs to satisfy economic, environmental, and social demands. The connection between civil urban infrastructure and smart cities is strong due to the common goal of fulfilling public service demands. Infrastructure management contributes to the development, evolution, and sustainability of smart cities. The main problem with traditional approaches to the development, evolution, and sustainability of smart cities is the lack of a holistic, integrated vision of infrastructure management. The main objective of this research is to introduce an infrastructure management humanistic approach with a smart city conceptual model that also considers an educational perspective. A mixed research methodology that combines quantitative and qualitative approaches was used, applying inductive-deductive tools. The paper concludes with the development of an infrastructure management framework for smart cities with five dimensions: (1) Environmental, (2) financial-economic, (3) political-governance, (4) social-people, and (5) technological. Two case studies for the cities of Lima and Piura in Perú illustrate how to incorporate this framework into practice. The research products are relevant because they foster an inclusive better quality of life for all citizens by preserving civil infrastructure systems.

Lessons Learned and Recommendations for the Application of Systems Engineering as an Emerging Discipline in Transportation & Infrastructure Projects

AbstractWhile systems engineering (SE) has been a well‐established discipline focusing on interdisciplinary systems and engineering management of complex systems over their life cycles, SE is still widely unknown in the U.S. infrastructure industry.The U.S. has recently passed a $1 trillion infrastructure bill (H.R. 3684), calling for investments in roads, bridges, rail, ports, airports, power, water, broadband, and other major projects. It is the intent of this paper to provide practical guidance to infrastructure owners and operators who are interested in reaping the benefits of applying SE to their transportation and infrastructure projects.This paper memorializes the lessons learned from over a of decade of real‐world, hands‐on experience of introducing and gradually increasing the application of systems engineering to building the civil infrastructure of the California High‐Speed Rail System (CHSRS), starting with the environmental impact review, preliminary engineering, final design, construction, inspection and testing, and finally the certification and planned handover of 119 miles of civil work and over 225 individual structures to the following track and systems contracts.As the first three CHSRS civil works construction packages (CP) are currently nearing completion, three new civil work and passenger station procurement contracts have recently been awarded, extending CHSRS to 171 miles and close to 300 structures, including four passenger stations, with additional track and systems, trainset, and train operator contracts planned in the near future.The extension of the CHSRS presented a timely opportunity to incorporate the SE lessons learned during the first three construction packages and update the systems engineering process requirements for the new CHSRS extension projects going forward. The SE requirements are presented in form of Systems Development Life Cycle (SDLC) requirements and have been prepared as tailored requirements for both civil infrastructure and track and systems contracts.This paper intends to share the lessons learned and present them as specific and actionable recommendations, providing practical guidance for the application of SE to transportation and/or infrastructure projects.

Role of recovery profile dependency in time‐dependent resilience

AbstractResilience assessment of civil structures and infrastructure systems plays a vital role in quantitatively measuring the object's ability to withstand and recover from disruptive events (e.g., natural hazards). The performance of the object (structure or system) may degrade with time, and the external loads often display nonstationary characteristics with time‐varying occurrence rate and/or magnitude. In this context, the resilience would be dependent on the duration of the reference period of interest, known as time‐dependent resilience. This article investigates the effect of recovery profile dependency on the time‐dependent resilience of structures and systems. The term “recovery profile dependency” refers to the association of the post‐hazard recovery processes in the aftermath of different load events within the object's service life, and is reflected through the following three aspects: (1) the temporal correlation between the remaining functionalities associated with different load events; (2) the load–recovery interaction (i.e., the scenario that a load event occurs before the full recovery of the degraded functionality due to the previous load event), and (3) the resource allocation strategy in the presence of limited resources, which affects the expeditiousness of each recovery process. In particular, for (2), the concept of average ratio of interaction is proposed to measure the frequency of load‐recovery interaction. The evaluation of time‐dependent resilience is demonstrated through two examples. It is shown that, the time‐dependent resilience is sensitive to the impact of load–recovery interaction and the selection of resource allocation strategy, but is insensitive to the temporal correlation of the remaining functionalities.

A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure system

A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure system

Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure.

Creating multifunctional concrete materials with advanced functionalities and mechanical tunability is a critical step toward reimagining the traditional civil infrastructure systems. Here, the concept of nanogenerator-integrated mechanical metamaterial concrete is presented to design lightweight and mechanically tunable concrete systems with energy harvesting and sensing functionalities. The proposed metamaterial concrete systems are created via integrating the mechanical metamaterial and nano-energy-harvesting paradigms. These advanced materials are composed of reinforcement auxetic polymer lattices with snap-through buckling behavior fully embedded inside a conductive cement matrix.Werationally design their composite structures to induce contact-electrification between the layers under mechanical excitations/triggering. The conductive cement enhanced with graphite powder serves as the electrode in the proposed systems, while providing the desired mechanical performance. Experimental studies are conducted to investigate the mechanical and electrical properties of the designed prototypes. The metamaterial concrete systems are tuned to achieve up to 15% compressibility under cycling loading. The power output of the nanogenerator-integrated metamaterial concrete prototypes reaches 330 µW. Furthermore, the self-powered sensing functionality of the nanogenerator concrete systems for distributed health monitoring of large-scale concrete structuresisdemonstrated. The metamaterial concrete paradigm can possibly enable thedesignof smart civil infrastructure systems with a broad range of advanced functionalities.

Urban-Level Data Interoperability for Management of Building and Civil Infrastructure Systems during the Disaster Phases Using Model View Definitions

Urban-Level Data Interoperability for Management of Building and Civil Infrastructure Systems during the Disaster Phases Using Model View Definitions

Risk-Informed Bridge Optimal Maintenance Strategy Considering Target Service Life and User Cost at Project and Network Levels

As the concept and methods of maintenance of infrastructure systems are receiving increasing attention, the optimal maintenance strategy for a group of structures is a promising research topic. Among the factors affecting the decision-making of optimal maintenance strategy for civil infrastructure systems, the target service life and user cost estimation approach are significant. Currently, user cost estimation at the project level is widely adopted, whereas the user cost estimation at the network level is in the development stage. This paper investigates the impact of several factors on the risk-informed optimal maintenance strategy for a bridge network under corrosion, including target service life, user cost estimation approach, and correlation among bridge safety margins. The novelty of this paper consists of considering the effects of these factors on the optimal maintenance strategy of bridge networks. These effects, which are crucial for adopting the optimal maintenance strategy, were not investigated in a detailed manner previously. Two different maintenance strategies are considered for the maintenance of corroded steel girders. The former is to conduct replacement with new carbon steel girders, and the latter is to use A709-50CR steel, where CR stands for corrosion resistant, a new type of steel with a chromium content similar to that of martensitic stainless steel, in replacement actions. Using an existing bridge network under a low life-cycle risk threshold, it is shown that replacement based on A709-50CR girders results in a reduced life-cycle network maintenance cost than replacement based on carbon steel girders. Also, the project-level approach to estimate user cost can lead to a substantial increase in the life-cycle network maintenance cost compared with the network-level approach.