ASCE OPEN: Multidisciplinary Journal of Civil Engineering

The service life and performance of the civil infrastructure are affected by the changing climate, and the changing climate features significant uncertainties that require rigorous consideration and quantification so they can be incorporated into the reliability assessment and risk management. A lack of quantification makes it difficult for the stakeholders, designers, and operators of the infrastructure to implement the appropriate decisions for mitigating the risks exacerbated by the changing climate. In this paper, a state-of-the-art review is conducted on existing studies in the literature concerning the influence of climate change on the risk assessment of concrete civil infrastructure. The review covers the following key aspects: (1) climate variables and the associated uncertainties; (2) frequency and intensity of natural hazards under various future climate scenarios; (3) the impact of climate change on the deterioration mechanisms of concrete structures; (4) the risk assessment methodology considering climate change; (5) climate-related multihazard risk assessment; and (6) adaptation strategies for the increasing risk of failure of civil infrastructure caused by climate change. The basic concepts, research development, and challenges concerning the impact of climate change on concrete infrastructure are comprehensively discussed. The review can benefit future research in the field of concrete infrastructure, especially on topics related to structural performance, durability, and risk assessment. Additionally, it will contribute to promoting appropriate adaptation planning and risk-based decision-making for the designers and operators of concrete infrastructure under the changing climate.

Recent advancements in vision-based visual inspection enable the identification, localization, and quantification of damage on structures. However, existing damage quantification methods are limited to measuring one- or two-dimensional attributes such as length or area, which is insufficient for certain damage types such as spalling that require depth in addition to in-plane measurements, as outlined in inspection manuals. To address this limitation, we propose utilizing image-based dense 3D reconstruction to perform full 3D quantifications to assess damages for concrete structure inspections. The proposed method is applied to quantify spalling damage in 3D to compute volumetric loss and maximum depth of the damage in line with bridge inspection manuals. Our approach involves using a convolutional neural network-based interactive segmentation algorithm to accurately segment spalling boundaries from images. Structure-from-motion and multiview stereo algorithms are then applied to generate a detailed 3D point cloud reconstruction of the spalling using multiple images. From this point cloud, a 3D mesh representation of the spalling is created for precise quantification. To validate our proposed technique, we conducted laboratory and field experiments to capture images and interactively segment the damage. The results demonstrate the effectiveness and reliability of our approach for 3D damage quantification in structure inspections.

Across the United States, rural households are more vulnerable to higher energy burdens (percentage of household income spent on energy bills). Energy-efficient and renewable energy technologies provide the means for improving energy performance and reducing the operating costs of residential buildings. While there is significant evidence regarding their benefits, the investment in these technologies can be significantly lower in rural areas. In this study, we analyze the adoption behaviors of rural households concerning five technologies: Smart thermostats, light-emitting diode (LED) lighting, weatherization, Energy Star-rated appliances, and rooftop solar panels. Previous research on the motivations and barriers underlying the adoption of these technologies has primarily focused on urban and suburban areas. This study adopted a qualitative approach in eight rural Iowa communities (Boone, Nevada, Pella, Solon, Swisher, Williamsburg, Kelley, and Elkhart), where 39 (N = 39) rural homeowners were interviewed. Our findings demonstrate that motivations to adopt energy-efficient and renewable energy technologies are driven by reduced energy costs, local availability of appliances or contractors, and environmental impact. By contrast, barriers to the adoption of these technologies are largely driven by high costs, insufficient information about the technologies, and the local availability of appliances or contractors. The qualitative data obtained from the interviews moves beyond survey-based data, providing inductive explanations for adoption behaviors among rural households. While the findings presented herein are designed to increase the awareness of energy-efficiency program administrators and policymakers, the paper does not suggest that the results and discussion are necessarily applicable to all rural regions. Rather, these results are primarily applicable to communities across rural Iowa.

The paper introduces a generic deep learning–based method using a sliding window computing algorithm based on long short-term memory (LSTM) networks for the classification of potential anomalies (e.g., cracks, potholes, bumps, and uneven surfaces) in support of the development of an instrumented bike. The instrumented bike provides a real-time platform to sense, store, transmit, and analyze cycling information through a sensor logger, smartphone, and the proposed LSTM-based sliding window computing algorithm. The paper is to address concerns with respect to existing factors such as weight of cyclists, speeds, types of bikes, and threshold setting that have an impact on the accuracy of identification of potential anomalies during instrumented cycling activities. The LSTM-based sliding window computing algorithm is designed in a way that it analyzes and localizes anomalies without any human-controlled supervision (threshold setting) while achieving human-level perception. Two bike routes were selected to validate the effectiveness of the sliding window computing algorithm in the identification of anomalies involving four cyclists. Based on the computing results from the two field tests, the numbers of distressed pavement areas from the four cyclists were 53, 51, 46, and 48, respectively. The follow-up p-value of ANOVA test result is 0.98, indicating the difference in detected anomalies among the four cyclists is not significant. Therefore, the paper concludes that the LSTM-based sliding window computing algorithm has the ability to effectively detect anomalies of cycling trails and it also provides an effective and efficient technique to replace the human-made threshold setting in support of the development of instrumented bikes and promote cycling as a daily mode of transportation.

The distribution and inequalities of flood risks across space and social groups are important considerations for infrastructure design and adaptation. However, neither civil infrastructure practitioners nor researchers routinely assess inequalities quantitatively, which limits opportunities to address them. The Lorenz curve method has been used in many fields to study inequalities in both resource and risk distributions across populations, and it supports a quantitative measure of inequality, the Gini index. Here we present the formulation of Lorenz curves and a data analysis workflow to measure inequalities in both flood damages and flood exposure using flood hazard, flood damage, and social data. We define flood exposure as flood depth multiplied by population density, whereas flood damages are taken strictly as a measure of economic losses. We then show that Lorenz curves for flood damages have the potential to differ markedly from Lorenz curves for flood exposure since the latter is dependent on the population density. For example, higher population density reduces per person damages but increases total flood exposure. Furthermore, we illustrate the Lorenz curve method at scale with an application to Los Angeles County, home to nearly 10 million people. These results demonstrate both similarities and differences between flood damage and flood exposure inequalities for different racial and ethnic groups, and they also show that regional inequalities are sensitive to changes in flood severity and the capacity of flood infrastructure. These sensitivities illuminate the complexity of achieving social equality in flood adaptation, and they emphasize both the utility of detailed modeling and the need for community-engaged decision-making to evaluate and implement response measures. A review of state and federal flood assistance programs reveals heightened opportunities to shape public investments with quantitative measures of flood risk inequalities.

To successfully deliver infrastructure projects, it is essential to prevent corruption—i.e., the act of offering, giving, receiving, or soliciting anything of value, directly or indirectly, with the intention of improperly influencing the actions of another party in the procurement of financed projects. However, there is little to no research exploring the institutional- and project-level determinants of corruption in infrastructure projects. To fill this gap, this research develops a conceptual model that shows the effects of contract size, repeat winners, and public sector corruption on project corruption. Using data collected from the World Bank and Varieties of Democracy (V-Dem) database, 330 projects located in 61 countries between 2000 and 2018 were analyzed using multilevel logistic regression models. The results show the average contract size and the number of repeat winners in a project are important determinants of corruption risk in infrastructure projects. Higher levels of corruption in the public sector also significantly magnify the likelihood that increases in contract value will result in project corruption. This research deepens our understanding of corruption in infrastructure projects across diverse institutional environments and shows project managers and policymakers how different project characteristics influence the likelihood of corruption occurring in these localized settings.