Critical Infrastructure Assets Research Articles

Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines

Abstract. High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.

Analyzing the Highway Failure & their Maintenance a Case Study

Highways provide facilitating transportation with connecting regions, and developed economic growth. The failure of highway decreases the efficiency of road. These critical infrastructure assets are crucial for ensuring their higher functionality, comfort, and life span. This abstract provides a comprehensive overview of the analysis of cause of failure and to understand the role of highways and the significance of effective maintenance practices. A good study starts by the examining the importance of highways as key elements of transportation networks, supporting the movement of people, goods, and service facilities. It highlights their contribution to economic development, regional connectivity, and accessibility. Furthermore, the paper investigates the various factors that influence the performance and lifespan of highways, including traffic density, environmental factors, and design importance. The abstract further examines the challenges and considerations associated with highway maintenance, such as funding constraints, resource allocation, and the utilization of advanced technologies. It explores the importance of data-driven decision-making processes, asset management systems, and the adoption of innovative approaches like predictive maintenance to optimize maintenance practices. By analyzing case studies and existing literature, this study identifies best practices and lessons learned from successful highway maintenance initiatives around the world. It emphasizes the importance of proactive and well- planned maintenance programs, collaboration between stakeholders, and the integration of sustainable practices to ensure the longevity and cost-effectiveness of highways. In conclusion, this abstract presents a comprehensive analysis of the role of highway failure and their maintenance. It highlights the significance of maintaining these critical transportation assets to support economic growth factor, regional connectivity, and overall societal well-being. The findings provide valuable insights for policymakers, transportation agencies, and infrastructure professionals involved in managing and maintaining highway networks. Key Words: Safety, life span, analysis, significance, performance, lifespan, traffic volume, environmental conditions, design characteristics, routine maintenance, major maintenance interventions

On the influence of infrastructure availability on companies decisions toward modal shift and relocation of falicities

PurposeReduced availability of transport infrastructure affects highly dependent global supply chains (SCs), leading to modal shifts in the short term. Since relocation decisions of facilities may result in the long term, this paper evaluates companies’ business decisions in reaction to availability reductions of inland waterway transport (IWT). MethodologyA transport model evaluates the impact of reduced infrastructure availability through heuristic optimization based on the Traveling Purchaser Problem. The resulting increase in operational costs is used to assess the probability of relocating facilities based on a Facility Location Problem (FLP) which enables deriving the benefit from infrastructure conditions. FindingsThe study identifies critical thresholds for infrastructure availability that affect companies’ relocation decisions regarding the maintenance of public infrastructure. The case study exhibits actual critical infrastructure assets. Practical implicationsInsights into the decisive consequences of companies’ decisions are given, and awareness of the relevance of infrastructure investments on local areas’ attractiveness is raised. The results imply considering public infrastructure investments in maintenance for private business locations. OriginalityThe paper highlights a new way to sustain local industries and connects short-term agility and long-term resilience with companies’ decisions and the exogenous factor infrastructure availability. The applied use case focuses on the barely studied waterway infrastructure that gains importance in light of sustainability and climate change.

Machine learning in coastal bridge hydrodynamics: A state-of-the-art review

Coastal bridges are vulnerable to complicated hydrodynamics induced by hostile natural hazards, relevant research is thus required to ensure the safe operation of these critical infrastructure assets. Although coastal bridge hydrodynamic analyses can be carried out through commonly used approaches such as theoretical studies, numerical simulations, and experimental tests, they cannot fulfill the ever-increasing demand for applicability and computational efficiency. The recently advanced machine learning (ML) has emerged as a disruptive technology that fully revolutionized various scientific disciplines, resulting in a new paradigm to meet modern research needs. Aiming to provide the research community with holistic information on the key ingredients and current state-of-the-art of applying ML algorithms to coastal bridge hydrodynamics, this study presents a comprehensive review of the deployment of ML in coastal bridge hydrodynamics. The theoretical backgrounds of some representative ML algorithms are briefly introduced, and applications of ML to each of the research themes associated with coastal bridge hydrodynamics are systematically surveyed. Future research directions are also highlighted through the discussion of the current research limitations. According to this review, it is evident that ML models can be trained to learn and infer the intricate relationships between contributing parameters and responses of interest in coastal bridge hydrodynamics. In addition, it is envisioned that the research in coastal bridge hydrodynamics could be further advanced with the evolving ML technologies.

Compound Coastal, Fluvial, and Pluvial Flooding During Historical Hurricane Events in the Sabine–Neches Estuary, Texas

AbstractWhen storm surge, river discharge, and rainfall occur simultaneously or in succession, they may cause compound flooding and exacerbate impacts in coastal communities. In traditional modeling approaches, these driving processes are simulated separately, leading to discrepancies between simulated and observed data and highlighting the need for coupled modeling frameworks. Here, we develop a coupled coastal–fluvial–pluvial model for the Sabine–Neches Estuary in southeastern Texas using Delft3D to simulate flood extent and depth during Hurricanes Rita, Ike, and Harvey. For each hurricane, four scenarios (i.e., coastal, fluvial, pluvial, and compound) are modeled to determine the relative contribution of each driving force to water levels across the domain. Our results reveal that tropical cyclones such as Harvey that are accompanied by extended periods of rainfall produce the largest extent and longest duration of compound flooding. Such storms also produce highly dynamic and evolving patterns of compound flooding over their duration, with multiple flood peaks that can inhibit response and recovery efforts. When simulating these storms, a coupled model can provide substantial improvements over coastal‐, fluvial‐, and pluvial‐only models, decreasing root mean square error by 69%, 69%, and 79%, respectively. In contrast, storms with short‐duration rainfall and moderate to severe storm surge produce less extensive compound flooding, which is typically dominated by coastal–pluvial interactions. Our results further highlight the potential for compound flooding at the locations of critical water infrastructure assets, suggesting the need for flood adaptation approaches that are robust to multiple types of flooding.

Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data

A novel framework for the expedient assessment of flood risk to transportation networks focused on the response of the most critical and vulnerable infrastructure assets, the bridges, is developed, validated and applied. Building upon the recent French guidelines on scour risk (CEREMA, 2019), this paper delivers a thorough methodology, that incorporates three key, risk parameters: (i) the hydrodynamic loading, a hazard component of equal significance to scour, for the assessment of hazard; (ii) the correlation of select scour indicators with a new index relating to flow velocity, a primary measure of the adverse impacts of flow–structure interaction, enabling a more accurate and automated, assessment of bridge susceptibility to scour; (iii) the use of a new, comprehensive indicator, namely the Indicator of Flood Hazard Intensity (IFHI) which incorporates, in a simple yet efficient way, the key parameters controlling the severity of flood impact on bridges, namely flow velocity, floodwater height, flow obstruction, and sediment type. The framework is implemented for the analysis of flood risk in a case study area, considering an inventory of 117 bridges of diverse construction characteristics, which were affected by a major flood that impacted Greece in September 2020. The reliability of the method is validated against an extensive record of inspected and documented bridge damages. Regional scale analysis is facilitated by the adoption of the Multi-Criteria Decision-Making method for flood hazard indexing, considering geomorphological, meteorological, hydrological, and land use/cover data, based on the processing of remotely sensed imagery and openly available geospatial datasets in GIS.

Whale optimization algorithm-based point cloud data processing method for sewer pipeline inspection

Sewerages are critical infrastructure assets for wastewater carriage in urban lifelines, but their function can be seriously affected by blockages or deterioration. Existing sewer pipeline inspection methods, such as closed-circuit television and sonar detection, have been blamed for low efficiency and considerable noise in the collected data. Therefore, this paper attempts to enhance the blockage and deterioration assessment inside the sewer pipeline by proposing a whale optimization algorithm-based point cloud data (WOAPCD) processing method. The method consists of an improved WOA for data clustering and fitting and a reverse slicing method for modeling the as-is conditions. The applicability of this proposed approach is validated in an actual sewerage system, and the results show that the WOAPCD can accurately and effectively reconstruct the 3D model of the sewer, providing valuable information for quantifying siltation conditions. The proposed method has better performance than PSO and GA in terms of the fitting error and modeling speed.

Detection and Estimation of Natural Gas Leakage Using UAV by Machine Learning Algorithms

Presently, unmanned aerial vehicles (UAVs) have a broad spectrum of applications, in which regular monitoring of critical infrastructure assets is an important risk mitigation usage. This paper presents a novel approach for modeling leakage from natural gas pipelines using machine learning algorithms operating on the gas leakage detection (Methane) data collected using an UAV having a gas sensor and a light detecting and ranging (LiDAR) payload. The detection system has a lightweight design along with a small form factor to ensure compatibility with most autonomous mobile platforms like UAVs or wheeled robots. Two experiments were conducted to collect the gas leakage detection data at various loitering heights in real atmospheric conditions before applying the estimation algorithms. The first experiment measured natural gas leakage data in varying loitering radiuses and altitudes for leakage pressures around one pound per square inch (PSI), while the second experiment did the same for leakage pressures around two PSI. Real atmospheric conditions were incorporated along with additional aspects of propeller down-wash and environmental air movements that can influence leakage detection. Two estimation algorithms, viz., reduced support vector machine (RSVM) and artificial neural network (ANN) were applied to the leakage data collected from the UAV experiments. It was found that the ANN approach resulted in more faster and accurate detection than RSVM that heavily depended on the kernel function for its performance. The efficacy of leakage detection of natural gas using a UAV payload was demonstrated, which is a faster and cost-effective alternative to the manual inspection process.

Importance of hospital facilities management performance indicators: Building practitioners’ perspectives

Hospitals are critical infrastructure assets and the strategic management of facilities within these buildings are quintessentially important to society who rely upon effective healthcare services. Despite their importance to the optimised functioning of hospital facilities, standardized performance evaluation measures such as key performance indicators (KPIs) have hitherto received scant academic attention. Understanding the views of practitioners working on the different lifecycle stages of a building will help to establish pragmatic KPIs for hospital facilities management (FM). Hence, a multi-stage study was undertaken, within which interpretivism and inductive reasoning was utilised to conduct a systematic review of extant literature on hospital FM and KPIs. In parallel, the initial stage of work shortlisted 18 KPIs, in four categories (physical, safety, environmental and financial), as essential for hospital FM performance evaluation. Using these indicators, a questionnaire survey was developed and disseminated to building practitioners in the hospital sector of Hong Kong. Data gathered was then analysed using both summary and inferential statistics. The analyses reveal that the practitioners generally regarded the physical and financial indicators as more important. When compared between the perspectives of the design/construction practitioners and the FM group, significant differences were found with three particular KPIs: availability of fire services system, energy utilization index and carbon emissions per building area. The paper concludes with direction for future research that seeks to analytically determine the importance weighting of each performance indicator identified, which is requisite for establishing a credible hospital FM performance evaluation scheme.

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

Toward regional hazard risk assessment: a method to geospatially inventory critical coastal infrastructure applied to the Caribbean

Hurricanes and sea level rise pose significant threats to infrastructure and critical services (e.g., air and sea travel, water treatment), and can hinder sustainable development of major economic sectors (e.g., tourism, agriculture, and international commerce). Planning for a disaster-resilient future requires high-resolution, standardized data. However, few standardized approaches exist for identifying, inventorying, and quantifying infrastructure lands at risk from natural hazards. This research presents a cost effective, standardized and replicable method to geospatially inventory critical coastal infrastructure land use and components, for use in risk assessments or other regional analyses. While traditional approaches to geospatial inventorying rely on remote sensing or techniques, such as object-based image analysis (OBIA) to estimate land use, the current approach utilizes widely available satellite imagery and a “standard operating procedure” that guides individual mappers through the process, ensuring replicability and confidence. As a pilot study to develop an approach that can be replicated for other regions, this manuscript focuses on the Caribbean. Small islands rely heavily on a small number of critical coastal infrastructure (airports, seaports, power plants, water and wastewater treatment facilities) and climate related hazards threaten sustainable development and economic growth. The Caribbean is a large and diverse area, and gaps exist between countries in the resources required for planning but much of the region lacks a comprehensive inventory of the land, infrastructure, and assets at risk. Identifying and prioritizing infrastructure at risk is the first step towards preserving the region’s economy and planning for a disaster resilient future. This manuscript uses high resolution satellite imagery to identify and geo-spatially classify critical infrastructure land area and assets, such as structures, equipment, and impervious surfaces. We identified 386 critical coastal infrastructure facilities across 28 Caribbean nations/territories, with over 19,000 ha of coastal land dedicated to critical infrastructure. The approach establishes a new standard for the creation of geospatial data to assess land use change, risk, and other research questions suitable for the regional scale, but with sufficient resolution such that individual facilities can utilize the data for local-scale analysis.

Criticality assessment for a regional maritime economy.

To identify and assess the criticality for infrastructure assets and better understand their dependencies, interdependencies, and supply chain reliance. This study used a modified mission impact, symbolism, history, accessibility, recognizability, population, and proximity model combined with a deliberative process with regional subject matter experts. Hampton roads Virginia maritime area. Emergency managers, US Corps of Engineers, US Coast Guard, law enforcement, railroad industry, intelligence community, Virginia Department of Emergency Management, Virginia Department of Transportation, and Virginia Port. A prioritized list of one-to-n critical assets in the maritime area and identification of up and down-stream dependencies. The final most highly critical grouping included 26 out of 277 assets including especially important road bridges and tunnels, rail bridges and choke points, shipping channels, and marine terminals. Subject matter experts identified 277 critical infrastructure assets in the Hampton Roads Maritime Area (HRMA). From these, 26 assets that were deemed to be significantly more critical than others. From this reduced list, 12 were further assessed to be most important. The selection process provided significant support to those responsible for providing protection, mitigating potential damage, and planning recovery and allows informed, objective expenditures of limited funding. Additional key findings include the following: Proximity, or the potential for an asset that has been damaged or destroyed to cause direct harm to adjacent assets in the surrounding community, drives criticality in the HRMA more than any other factor in the model. A small group of 26 out of 277 assets exerts an outsized impact in the HRMA-nearly 20 percent of criticality-due to the potential consequences associated with their destruction or disruption. Road transportation, particularly tunnels, represents the primary dependency among critical assets. Major road and maritime transportation assets rely heavily on federal and state organizations to maintain their function. Supply chain: The critical asset group supports or supplies many missions and industries both nationally and internationally, including defense, manufacturing, commercial enterprise, and the movement of raw commodities.

Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1

We present a comprehensive methodological framework for structural deformation monitoring of critical infrastructure assets based on differential SAR interferometry. By employing persistent scatterer interferometry, deformation time series in line-of-sight are derived from freely available Sentinel-1 single look complex products. These raw time series are analysed and refined using an extensive post-processing chain to obtain daily rates for vertical and horizontal deformation components. The post-processing includes cleaning, smoothing and a temperature correction to account for different sensing times in ascending and descending orbits. Longitudinal clustering of time series is used to reveal spatial patterns in the single epoch deformation series. Seasonal trend decomposition of the aggregated time series is performed to separate deformation trends from seasonal deformations. The applicability of the framework is showcased at the example of an integral concrete bridge located in the port of Vienna. Results are validated against in situ deformation measurements. The presented framework constitutes a blueprint for the continuous monitoring of critical infrastructure assets using satellite interferometry, which may supplement conventional structural health monitoring.

Methodology to Determine the Device-Level Periodicity for Anomaly Detection in EtherCAT-Based Industrial Control Network

Continuous operation and monitoring of critical infrastructure networks are crucial to ensure their sustainability and security. To achieve these, industrial control system (ICS) networks and supervisory control and data acquisition (SCADA) systems are deployed in critical infrastructure assets. Many events in ICS networks present strong periodical patterns because of process repetition or cyclic communication. From the security viewpoint, inferring the periodicity primarily in the device-level communications where the actuator/sensor, field, and the cell-level transmissions are performed is important for detecting anomalies. The synchronization period and traffic pattern need to be known for anomaly detection. This article presents a novel periodicity detection approach specifically for Ethernet for control automation technology (EtherCAT) networks. It uses protocol-specific operations and fields for detecting device-level periodicity. Using the period, an anomaly detection method that uncovers traffic pattern statistics is also proposed. The periodicity can be detected with different precision, significance levels, and lag sizes. Four programmable logic controller (PLC) programs were developed to demonstrate the feasibility of the periodicity detection approach, and the periodicity was automatically obtained with high accuracy. As any malicious activity on the system causes variances in the periodic pattern, the approach was also tested on synthetic traffic traces that contain denial of service (DoS) and code-injection attacks. The traffic patterns were uncovered by an anomaly detection module, which was developed on a Snort intrusion detection/prevention system (IDS/IPS) and used the captured period. The tests demonstrated that intrusions that exploit communication patterns are fully flagged.

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform’s functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making.

Analysis of Selected Assets against the Extraordinary Loads

The current security situation in the world creates pressure to improve the pro-tection of critical infrastructure assets against negative events, such as bomb attacks, industrial accidents, explosions, or fire. The research at the Faculty of Security Engineer-ing is focusing on such extraordinary loads. The present paper analyses selected assets against extraordinary load and describes their experimental testing.

Resilience assessment framework for critical infrastructure in a multi-hazard environment: Case study on transport assets

The exposure of critical infrastructure to natural and human-induced hazards has severe consequences on world economies and societies. Therefore, resilience assessment of infrastructure assets to extreme events and sequences of diverse hazards is of paramount importance for maintaining their functionality. Yet, the resilience assessment commonly assumes single hazards and ignores alternative approaches and decisions in the restoration strategy. It has now been established that infrastructure owners and operators consider different factors in their restoration strategies depending on the available resources and their priorities, the importance of the asset and the level of damage. Currently, no integrated framework that accounts for the nature and sequence of multiple hazards and their impacts, the different strategies of restoration, and hence the quantification of resilience in that respect exists and this is an acknowledged gap that needs urgently filling. This paper provides, for the first time in the literature, a classification of multiple hazard sequences considering their nature and impacts. Subsequently, a novel framework for the quantitative resilience assessment of critical infrastructure, subjected to multiple hazards is proposed, considering the vulnerability of the assets to hazard actions, and the rapidity of the damage recovery, including the temporal variability of the hazards. The study puts forward a well-informed asset resilience index, which accounts for the full, partial or no restoration of asset damage between the subsequent hazard occurrences. The proposed framework is then applied on a typical highway bridge, which is exposed to realistic multiple hazard scenarios, considering pragmatic restoration strategies. The case study concludes that there is a significant effect of the occurrence time of the second hazard on the resilience index and a considerable error when using simple superimposition of resilience indices from different hazards, even when they are independent in terms of occurrence. This potentially concerns all critical infrastructure assets and, hence, this paper provides useful insights for the resilience-based design and management of infrastructure throughout their lifetime, leading to cost savings and improved services. The paper concludes with a demonstration of the importance of the framework and how this can be utilised to estimate the resilience of networks to provide a quantification of the resilience at a regional and country scale.

Multi-Scale Hierarchical CRF for Railway Electrification Asset Classification From Mobile Laser Scanning Data

A network of railway infrastructure is one of the most critical infrastructure assets for supporting the national economy and sustainable mobility. Safe and reliable maintenance of railway infrastructure is critical to ensure that rail systems run safely and punctually. Such maintenance requires regular surveying of railway assets, which typically relies on time-consuming and error-prone labor-centric visual inspection. In this paper, we propose a novel supervised method for automatically classifying electrification assets of railway networks using mobile laser scanning data. A hierarchical Conditional Random Field (CRF) was investigated in order to apply both smoothness constraint and spatial regularities, to improve the classification result made by local supervised classifiers. We use a multi-scale line representation of original data, which implicitly combines object geometry cues and makes computation efficient. Our approach focuses on learning the spatial regularities at multiple representation scales to thoroughly understand the railway electrification scene. The spatial regularities are formulated as relative spatial location in a middle range for different line primitive scales and relative displacement in a full range for the final coarsest line primitive scale. The experiment shows that learnt spatial regularities at full range with multi-scales can outperform the model with spatial regularities at limited local ranges.

Experimental and Numerical Study of Engineered Cementitious Composite with Strain Recovery under Impact Loading

An engineered cementitious composite, endowed with strain recovery and incorporating hybrid shape memory alloy (SMA) and polyvinyl alcohol (PVA) short fibers, was subjected to drop weight impact loading. Numerical simulation of the composite’s impact behavior was performed, and the model predictions agreed well with the experimental findings. Numerical and experimental investigations demonstrated that incorporating SMA fibers in the composite yielded superior impact resistance compared to that of control mono-PVA specimens. Heat treatment stimulated the SMA fibers to apply local prestress on the composite’s matrix owing to the shape memory effect, thus enhancing energy absorption capacity, despite the damage incurred by PVA fibers during the heating process. The superior impact performance of the hybrid composite makes it a strong contender for the construction of protective structures, with a potential to enhance the safety of critical infrastructure assets against impact and blast loading.

Foreground–background separation technique for crack detection

AbstractCurrent level‐2 condition assessment methods for critical infrastructure assets mostly rely on human visual investigation of visible damages and patterns at the structure surface, which can be a costly, time‐consuming, and subjective exercise in reality. In this article, a novel method for crack detection is proposed via salient structure extraction from textured background. This method first extracts strong edges and distinguishes them from strong textures in a local neighborhood. Then, the spatial distribution of texture features is estimated to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two key steps are fused to calculate the final structure saliency map for generation of the crack masks. This method was validated on a data set with 704 images and the outcome revealed an average f‐measure of 75% in detecting the concrete cracks that is significantly higher than two other baseline methods.