ASCE OPEN: Multidisciplinary Journal of Civil Engineering

The impacts of climate-related hazards are becoming a major concern for many people worldwide, especially those in vulnerable areas such as Puerto Rico. In September 2017, Hurricane Maria caused severe disruption to the island’s drinking water supply due to power outages, causing major problems for utility companies. This led to water insecurity, particularly among residents, who could not access safe, reliable, adequate, and affordable drinking water. Disaster-related water insecurity challenges are coupled with widespread public mistrust of tap water, yet some residents still consume tap water despite the mistrust. Alternatively, a portion of those who trust the tap water quality choose not to consume it. This knowledge–behavior gap needs to be explored to understand tap water consumption behaviors in the context of mistrust and insecurity. This study’s main goal was to identify why residents mistrusted their tap water and their behaviors in response to or despite mistrust. Data collection included household surveys and interviews with residents (n = 154) from May 2022 to July 2022. Thematic qualitative analysis shows residents generally mistrust tap water because of its poor quality over the past decade based on its palatability properties (taste, color, and smell). In addition, people trust or mistrust tap water because of their lived personal positive or negative experiences with the water utility service in Puerto Rico. This study can be used to develop strategies to address water insecurity and understand public trust in the tap water supply provided by water utilities. Ultimately, this research emphasizes the need for more studies to explore the knowledge–behavior gap in order to understand why some people consume tap water despite the mistrust and vice versa.

Abstract

The 2018 flood experienced by Kerala, the southernmost state of India, has affected thousands of lives, resulting in heavy financial loss (248,930 million INR) and structural damage (129,420 residential buildings have been reported to be damaged). Depth versus damage relationships have been used conventionally for flood emergency decision-making; however, these are approximate and do not account for all the significant factors involved in the flood damage assessment. An extensive questionnaire survey was conducted in this study to collect data comprising significant parameters of flood-damaged residential buildings due to the 2018 Kerala flood event. The damage to the buildings was expressed in terms of damage ratios, and the buildings were classified into various damage states according to their damage ratios. Machine learning (ML) techniques, such as random forest, naïve Bayes, decision tree, K-nearest neighbors, AdaBoost, XGBoost, support vector machine, LightGBM, and CatBoost, were used in this study to develop models to predict and classify the buildings into various flood damage states. Results indicated that almost all the tree-based ML techniques performed well, and the random forest model was obtained as the best-performing classification model for flood damage prediction with 84% accuracy. Further SHAP (SHapely Additive exPlanations) analysis was conducted to explore the relative importance of the different parameters involved in damage prediction.

Practical Applications

Flood damage assessment of residential buildings is crucial for emergency recovery operations and disaster management. Conventionally, postflood damage assessment around the world is carried out through field inspection by an expert team immediately after the event, which is physically tiresome, costly, and time-consuming. The machine learning (ML)-based methods proposed in the present study can be used effectively for rapid postflood damage assessment of residential buildings. The buildings with high-risk category can be identified easily with the proposed ML models. This enables the authorities for efficient decision-making for proper allocation of the emergency response and rescue operations. The same system can be used very effectively incorporating GIS tools for pre- and postflood damage assessment. The proposed method can contribute to the prediction of flood damage cost provided the data related to flood depth, duration, and the data related to building are available. While the flood depth and duration data can be made available from the flood forecasting models, the building related data can be obtained from the district administration.

Enhanced designs of the built environment are particularly urgent for frontline communities, who experience the first and worst of climate change. A climate adaptation approach that prioritizes need-based distributive justice, the allocation of resources to meet the basic conditions necessary for continued community existence, may empower frontline communities in the resource allocation process. Need-based distributive justice is often viewed as a supplement to other universal principles of distributive justice, such as merit—the allocation of resources according to individual contributions. The overlooked need principle, however, is especially important because it can address the deficit of resources that frontline communities experience. Frontline communities disproportionately comprise members of racial minority groups, whose history of marginalization intertwines with past and present decisions that contribute to the position of communities on the frontlines of climate change. Yet, racial history is not commonplace in discussions about climate adaptation. Therefore, this research considers ways to introduce place-based racial history in climate adaptation planning. In an online experiment (n = 1,731), all participants were shown a video describing how updated infrastructure (e.g., cooling pavements) could address present and future heat vulnerability in a frontline community. The intervention, a 50-s addition to the video, described a highway in the community, which bisected and is associated with the decline of a historically Black neighborhood. Participants then shared their support for or opposition to a need-based distributive justice strategy for the project. The brief intervention tested did not significantly (P = 0.07) increase support for a need-based distributive justice strategy. However, additional analysis revealed that this relationship was mediated by the degree to which participants perceived the racial history presented to be relevant to the stated problem. These results suggest that more in-depth racial history interventions should be tested for this purpose.

Natural infrastructure (NI) and nature-based solutions in urban riverscapes can provide a spectrum of environmental, societal, and economic benefits, but widespread implementation of NI strategies remain limited because of their context-dependent nature. Windows of opportunity have opened through legislation and funding to expand NI solutions that address flooding, water quality, air pollution, extreme heat, and environmental equity. System-level approaches may offer these projects a framework that is flexible yet holistic enough to streamline implementation. In fact, a systems approach is essential to realize the potential of NI for equitably achieving these goals, and a critical step includes identification of vulnerabilities (e.g., exposure to environmental harm). The purpose of this study was to support decision makers and managers in prioritizing their urban riverscapes with multiple vulnerabilities: flood risk, water quality, ecosystem function, and environmental inequity. We conducted an urban stream spatial multicriteria decision analysis (MCDA) case study with Charlotte–Mecklenburg Storm Water Services to support equitable and efficient stream reach, floodplain, and watershed planning. Our study assessed the social and ecological characteristics of the system and prioritized vulnerable watersheds and subbasins using a spatial MCDA. We developed an urban stream prioritization framework that could be tailored to complement existing management strategies and also more broadly implemented in other social–ecological systems.

Abstract

Location-specific, near-term (≤10 years) temperature and precipitation conditions are informative and important in many infrastructure engineering activities. Two alternative approaches for obtaining near-term future climate change conditions—decadal predictions from global climate models (GCMs) in the Decadal Climate Prediction Project and observation-based extrapolations from a statistical forecasting model (autoregressive integrated moving average, ARIMA model)—were used and assessed for engineering applications. The climate model predictions and statistical extrapolations were obtained for 20 US cities across climate regions and estimated as four annual temperature and precipitation indices (e.g., annual heating/cooling degree days and maximum 1-day precipitation) and as joint distributions of annual temperature and precipitation changes commonly used in engineering studies. Quantitative assessments suggest that the two approaches generally provide comparable results and can be more accurate than common baseline methods of using historical data and assuming climate stationarity. Year-to-year climate variability can lead to large errors for both approaches, suggesting that obtaining probabilistic predictions or extrapolations is important and informative to quantify uncertainty. Applying climate model predictions in engineering currently involves significant time and effort for tasks such as downloading and processing large GCM prediction files. To facilitate the design, construction, and operation of infrastructure projects with near-term climate change information, observation-based statistical extrapolations of location-specific climate data provide a computationally and procedurally efficient option for engineering applications.

Practical Applications

Ensuring the resilience and reliability of infrastructure amid continuously evolving climate change requires obtaining and implementing future climate information in engineering practice. Two alternative approaches of obtaining regional climate information were assessed for near-term (≤10 years) engineering applications: climate-model-based predictions and local-historical-data-based extrapolations. Climate model predictions were acquired from the recently developed prediction results based on state-of-the-art, process-based global climate models. The observation-based approach directly extends the historical trend exhibited in local temperature and precipitation data. Several temperature and precipitation variables—commonly used to inform and facilitate engineering designs and analyses—were calculated using these two approaches and assessed with respect to accuracy and accessibility. In general, the two approaches have comparable accuracy and are more accurate (especially in recent years) than using long-term average historical conditions, and both approaches provide a practical means to quantify uncertainty from climate variability, which can be important in a near-term timeframe. The use of climate-model-based predictions in engineering is currently restrained by large prediction files and multiple processing procedures involved. The observation-based approach can serve as an efficient option to obtain and apply near-term regional climate information in infrastructure engineering.

The safety and serviceability of in-service structures and infrastructure systems are often threatened by natural hazards. Asset owners/decision makers are thus concerned about the resilience of an object of interest (structure or infrastructure), that is, its ability to be in readiness for, to absorb, recover from, and adapt to disruptive events. This is particularly the case when considering the potential impacts of climate change, which may lead to nonstationary natural hazards in the future (e.g., increasing wind hazard in a changing climate). Moreover, in many occasions, the presence of concurrent multiple hazards may result in more severe performance reduction to structures/infrastructures, compared with the occurrence of single hazards. This paper proposes an innovative method for the time-dependent resilience assessment of structures and infrastructure systems exposed to the impacts of concurrent multiple hazards in a changing climate. The interaction between different types of hazards is reflected through the mutual dependency between the performance functions associated with these hazards. New insights into the time-dependent resilience problem are also provided through a new concept of the performance concern index (PCI). It is shown that the mean nonresilience (i.e., 1 minus the mean value of resilience), if small enough, can be approximated by the mean value of the average PCI over the time domain of interest. Two examples are presented to demonstrate the applicability of the proposed method, and to investigate the sensitivity of resilience to key factors such as the interaction between multiple hazards and the climate change scenario.

The implementation of building information modeling (BIM), enabling the creation of digital database files containing semantic representation of civil structures and infrastructures, also concerns the field of road engineering. Differently from highly trafficked motorways, low-volume roads (LVRs) represent the largest part of the global road network, and this type of transportation infrastructure has received minimal attention in terms of BIM implementation in academic research. This work investigates the coordinated use of digital tools to enable the renewal of LVRs according to an integrated framework comprising georeferencing, alignment tracing, estimation of quantity take-offs, and evaluation of mechanical response as well as service life. This study ascertains a repeatable workflow using five systems: Autodesk InfraWorks, Autodesk Civil 3D, Autodesk Dynamo, COMSOL Multiphysics, and MATLAB. The research considers the rehabilitation of a Norwegian LVR located in Våler municipality as an application case study, the aim of which is to renew the road pavement by employing new aggregates for its reconstruction. This work assumes that the mechanical properties of the road construction materials are evaluated in the laboratory by means of repeated load triaxial tests. In this regard, six scenarios are envisaged: unstabilized, stabilized by a traditional binder (bitumen), and stabilized by four nontraditional polymeric binders (polyurethane, acrylate, styrene butadiene, and acetate). All five stabilization techniques lead to economic savings and improved mechanical performance. Compared with bitumen-treated aggregates, the adoption of nontraditional binders entails a longer road service life, although at a higher cost.

The Massachusetts Bay Transportation Authority (MBTA) Blue Line, a rail transit route serving communities in Greater Boston, is presently exposed to coastal flood risk and faces existential threats from future sea-level rise (SLR). Ensuring its resilience will require investments in climate adaptation. Planning such investments typically involves a consideration of several adaptation alternatives, evaluating each with respect to a variety of economic, technical, social, and environmental decision criteria, while simultaneously acknowledging the priorities and preferences of key stakeholders. Considering a set of archetypal resilience-enhancing adaptation alternatives for the Blue Line, we develop and implement a multicriteria decision analysis (MCDA) framework, evaluating the relative preferability of alternatives, while considering uncertainty in stakeholder priorities and risk preferences. Relying on the preferences of a set of sample stakeholders, we demonstrate a clear preference for a collaboratively executed exposure reduction strategy (shore-based protection measures), over other alternatives (floodwalls, cover/elevate, managed retreat) that could be implemented exclusively by the transit agency. Further consideration of the full range of stakeholder preferences can change the rank preference of adaptation alternatives, while uncertainties can be significantly reduced if stakeholders can reach a consensus on decision criteria and the level of risk aversion. The current MCDA framework can be readily extended to enable broader community participation, and hence, enhance procedural equity in adaptation planning.