Infrastructure Safety Research Articles

Network-level crash risk analysis using large-scale geometry features

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.

Concrete Surface Crack Detection Algorithm Based on Improved YOLOv8.

Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 109, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.

Urban Air Mobility for Last-Mile Transportation: A Review

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks.

Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology

Thermokarst landslides (TLs), which are made up of retrogressive thaw slumps (RTSs) and active-layer detachments slides (ALDSs), are quickly increasing in the central Qinghai-Tibet Plateau (QTP) permafrost area. TLs induce many environmental problems and threaten the safety of infrastructure. A landslide susceptibility map is crucial to prevent the negative impacts of these landslides. However, traditional thermokarst landslides susceptibility (TLS) evaluations do not consider real-time surface deformation information. Therefore, we propose a novel method that integrates a conventional machine learning model with ground surface deformation. Nine influencing factors, including slope, normalized difference vegetation index, elevation, precipitation, thawing degree days, soil content, active layer thickness, water content, and vegetation type were selected based on the q-index detector, and support vector machine was employed to obtain an initial model (IM). We obtained surface deformation in the study area using the enhanced Small Baseline Subset (SBAS) method. The accuracy of the InSAR results was validated through comparison with data from two field monitoring cross-sections. Subsequently, we established an integrated model (ITM) by combining the initial model with surface deformation using the contribution matrix, and confirmed the fusion model’s higher rationality and accuracy through comparison with the IM. Furthermore, we examined the impact of the quadtree segmentation method on atmospheric correction and validated the TLS results obtained using the ITM with high-resolution optical remote sensing imagery from GF6. Finally, the influencing factors and distribution characteristics of TLS was analyzed, and the results indicated that climatic conditions are the primary factors affecting the distribution of TLS.

Risks and Safety of CO2 Pipeline Transport: A Case Study of the Analysis and Modeling of the Risk of Accidental Release of CO2 into the Atmosphere

In the field of CO2 capture and sequestration, ensuring the safety of pipeline infrastructure is paramount to successful climate change mitigation efforts. This study investigates the dynamics of CO2 dispersion from pipeline systems, assessing not only the transport process but also the physical properties and associated hazards. Advanced simulation techniques are used to model how different states of CO2 (gas, liquid, and supercritical) and varying pipeline characteristics—such as perforation sizes, flow rates, and orientations—affect the dispersion patterns in the event of a leak. Simulations cover a range of atmospheric conditions, emphasizing the role of atmospheric stability and wind speed in shaping dispersion and defining potential impact zones. An analysis of historical pipeline accidents is included to inform risk management strategies. The results show that the orientation of the pipeline has a significant effect on dispersion, with downward leaks causing the largest impact zones, particularly under supercritical conditions. The results highlight the need for adaptive safety strategies that take into account real-time CO2 transport conditions and localized environmental data. By integrating these factors, the study recommends refining safety protocols and emergency response strategies to improve pipeline resilience and public safety against potential leaks. Key findings include the quantification of the relationship between leak parameters and dispersion areas, providing a valuable framework for future safety improvements.

Comprehensive analysis of correlations between soil electrical resistivity and index geotechnical properties

This study investigates the relationships between soil electrical resistivity and key geotechnical properties to develop predictive models for designing grounding systems in electrical substations. Conducted on 30 soil samples from various different regions of Thailand, the research evaluates resistivity in relation to soil water content, plasticity index, and dry density. Using a comprehensive laboratory setup, the soil samples were evaluated under controlled conditions, including temperature, humidity, and salt content of the soil samples, to establish robust correlations between the measured resistivity and the index geotechnical parameters. The results reveal a significant inverse relationship between soil resistivity and water content, confirming that water content is a dominant factor influencing resistivity. Further analysis using multiple non-linear regression demonstrated strong correlations, indicating that a combination of water content, soil dry density, and plasticity index can predict soil resistivity with high reliability. This approach enables optimization of substation grounding designs to ensure operational safety and regulatory compliance. The study suggests that integrating these geotechnical assessments into standard grounding system design procedures can significantly improve the effectiveness and safety of electrical infrastructure. Recommendations for future research include expanding the dataset to encompass a broader range of soil types and investigating the effects of particle size and electrolyte content on soil resistivity. These enhancements aim to refine the predictive models and address the broader applicability of the results in diverse geological settings.

Safe schools Africa: Ensuring the provision of safe infrastructure for school children on donor-financed road projects

Sub-Saharan Africa (SSA) has the world’s highest road traffic injury rates, and rates are increasing as the continent develops and mobilises. However, roads are a cornerstone of economic development, and so governments across the continent – supported by development partners – invest heavily in developing their road networks. No government or lending institution wants people – above all children, who are among the most vulnerable road users – to be injured or killed on the roads they finance and build. Making roads safe is not a controversial proposition. The challenge lies in changing the existing practices that allow high-risk roads to be built. This paper looks at a program, ‘Safe Schools Africa’, which seeks to address the challenge of ensuring that the existing road procurement, design, and construction systems and practices consider the specific road safety requirements of school children. The program ensures that provision is made to safeguard school children and other vulnerable road users in the design and construction of new and rehabilitated roads used by this group. Safe Schools Africa is operated and administered by the FIA Foundation and Amend. The FIA Foundation and Amend work together on advocacy and project identification, while Amend carries out the community engagement and technical engineering services and manages the logistics of project delivery. The paper will examine examples of the ongoing application of ‘Safe Schools Africa’ on donor-financed projects in Ghana, Mozambique, Tanzania, and Zambia and share some lessons learnt to date.

Omnidirectional optic fiber shape sensor for submarine landslide monitoring

In-situ submarine landslide monitoring is imperative for ensuring the safety of ocean infrastructures, although it poses greater challenges compared to conventional landslide monitoring methods. In response to this need, we developed an omnidirectional optic fiber shape sensor (OFSS) and a self-contained submarine landslide monitoring system. Wavelength division multiplexing (WDM) and space division multiplexing (SDM) are used to build 10 omnidirectional curvature sensors with 3 fiber Bragg gating (FBG) arrays. Difference curvature demodulation algorithm is introduced to eliminate bending-irrelative effects. The omnidirectional curvature transmission matrix (OCTM) is obtained through calibration process with standard curvature molds. Frenet algorithm is used to recovery the shape. Laboratory experiments demonstrate OFSS’s ability to monitor 3D shape in real time, achieving the deflection error of 0.23 % and the bending direction error of 0.06 rad in the simple bending shape recovery experiment. In the S-shape recovery experiment, the OFSS’s deflection error is 0.77 % and the bending direction error is 0.04 rad. Subsequently, the system underwent testing in shallow water within a ship dock. In this experiment, the OFSS is successfully penetrated into the seabed and remained embedded in the soil for 90 min. The maximal shift observed at the end of the OFSS is 4.61×10-3m with the standard deviation of 9.95×10-4m, while the bending direction exhibited a maximal shift of 3×10-3 rad with the standard deviation of 7.51×10-4rad over the course of the 90-minute duration. These results validate the system’s effectiveness in providing accurate and reliable in-situ monitoring of submarine landslide.

INUNDATION PREDICTION AS THE EFFECT OF RISING MEAN SEA LEVEL AND HIGH ASTRONOMICAL TIDE IN NORTH JAKARTA BASED ON 2013-2021 TIDAL DATA

Future sea level rise in North Jakarta is one of the potential hazards that threaten the safety of coastal communities and infrastructure. Prediction of sea level rise is needed to reduce the vulnerability of the people living on the coast. This study was conducted to estimate the height of sea level rise in 2045 using linear regression analysis of hourly tidal data for 2013-2021 coupled with the highest astronomical tide value from the results of tidal harmonic analysis in 2021 using the t_tide application in matlab. The water elevation was then projected on the Digital Elevation Model to obtain the inundation area in 2045. The result of this study is an inundation area of 56 km2 in North Jakarta in 2045 due to an increase in total sea level of 117 cm at high tide.

Eigenfrequency analysis using fiber optic sensors and low-cost accelerometers for structural damage detection

Structural Health Monitoring (SHM) is crucial for infrastructure safety and integrity. Arduino-based sensors are gaining popularity in low-cost SHM structures. Distributed fiber optic systems (DFOS), such as Distributed Acoustic Sensing (DAS), are employed for accurate SHM despite their high costs, computational demands, and energy consumption. The primary objectives of this work are to compare the accuracy of an accelerometer named LARA (Low-cost Adaptable Reliable Accelerometer (LARA)) that utilizes both Arduino and Raspberry Pi technologies with a DAS system in detecting structural damage and to explore the potential advantages of combining LARA and DAS to create an effective SHM tool. This study is the first to enhance the design of LARA. Subsequently, LARA and DAS were used in a laboratory setting to analyze eigenfrequency changes in a beam model with induced localized damage. Finally, this study evaluated the precision and reliability of LARA and its potential role as a trigger for DAS in detecting localized damage. The findings show that both LARA and DAS can identify changes in the eigenfrequencies of damaged structures with deviations as small as 3.68 %. Consequently, LARA demonstrated its potential as a trigger for DAS, significantly reducing the computational demands while enriching the analysis. This approach offers highly accurate eigenfrequency measurements and enhances the analytical capabilities of DAS by identifying the primary axes of the detected eigenfrequencies.

Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.

Prediction of Slope Safety Factor Based on Attention Mechanism-Enhanced CNN-GRU

This paper proposes a new method for predicting slope safety factors that combines convolutional neural networks (CNNs), gated recurrent units (GRUs), and attention mechanisms. This method can better capture long-term dependencies, enhance the ability to model sequential data, and reduce the dependence on noisy data, thereby reducing the risk of overfitting. The goal is to improve the accuracy of slope safety factor prediction, detect potential slope stability issues in a timely manner, and take corresponding preventive and control measures to ensure the long-term stability and safety of infrastructure and promote sustainable development. The Pearson correlation coefficient is used to analyze the relationship between the target safety factor and the collected parameters. A one-dimensional CNN layer is used to extract high-dimensional features from the input data, and then a GRU layer is used to capture the correlation between parameters in the sequence. Finally, an attention mechanism is introduced to optimize the weights of the GRU output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using metrics such as the mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root-mean-square error (RMSE), and R2. The results show that the CNN-GRU-SE model outperforms the GRU, CNN, and CNN-GRU models in terms of prediction accuracy for slope safety factors, with improvements of 4%, 2%, and 1%, respectively. Overall, the research in this paper makes valuable contributions to the field of slope safety factor prediction, and the proposed method also has the potential to be extended to other time-series prediction fields, providing support for a wide range of engineering applications and further promoting the realization of sustainable development.

Automatic crack segmentation model based on multi-branch aggregation transformer

Crack detection plays a crucial role in evaluating the safety and durability of civil infrastructure. However, detecting cracks of uneven intensity in complex backgrounds is challenging. To overcome this problem, we propose a dual decoder network (CSMT) based on a multi-branch aggregation Transformer, which uses residual atrous spatial pyramid pooling (RASPP) and Transformer dual decoding branches to extract local and global features of different structures. To enhance global feature extraction, we designed a multi-branch aggregation Transformer (MAT) that adaptively weights the features of two attention heads from spatial and channel dimensions to achieve intra block feature aggregation between dimensions. Meanwhile, to obtain multi-scale semantic information, we constructed a new decoding branch, RASPP, which embeds a squeeze-and-excitation (SE) module and residual structures into standard ASPP. Finally, we propose a feature adaptive fusion module (FAM) to enhance feature fusion between adjacent layers and codec layers. Many experiments on three benchmark datasets have shown that the proposed CSMT segmentation network provides excellent performance in a variety of complex scenarios.

Optimizing Integrative Holistic Early Childhood Development in Mimika Baru Sub-district: An IFAS and EFAS Analysis

This study investigates the implementation of Integrative Holistic Early Childhood Development (IHECD) in Mimika Baru Sub-district, focusing on four kindergartens: TK Negeri 1 Mimika, TK Kartika VI-31, TK Advent Timika, and TK Ceria Cerdas. Utilizing IFAS (Internal Strategic Factor Analysis Summary) and EFAS (External Strategic Factor Analysis Summary), the research aims to evaluate the current conditions and recommend strategies for optimizing IHECD. An evaluative study method was employed, involving questionnaires and focus group discussions to gather data on internal factors such as leadership, educator quality, and infrastructure, as well as external factors including government support and community involvement. The findings reveal that all institutions have more strengths than weaknesses and more opportunities than threats, placing them in quadrant I of the SWOT matrix, which indicates strong positions with significant growth potential. Despite these strengths, challenges such as inadequate infrastructure and insufficient health and safety measures were identified. The study underscores the need for tailored strategies to address these issues, suggesting that policymakers and stakeholders enhance support and resources to improve IHECD implementation. By focusing on strengthening internal capabilities and leveraging external opportunities, the overall quality of early childhood development services can be elevated, contributing to better educational outcomes and human resource development in the region. Furthermore, the research recommends a growth strategy for these institutions to maximize their strengths and opportunities. Future research should explore diverse settings, including rural areas, and consider longitudinal approaches to assess the long-term impact of recommended strategies. This comprehensive approach will ensure that all children in the Mimika Baru Sub-district have access to quality early childhood development services.

Early Detection and Stability Assessment of Hazardous Rock Masses in Steep Slopes

The assessment of slope stability plays a critical role in the prevention and management of slope disasters. Evaluating the condition and stability of hazardous rock masses is essential for predicting potential collapses and assessing treatment effectiveness. However, conventional measurement techniques are inadequate in high slope areas, which lack sufficient spatial data to support subsequent calculations and analyses. Therefore, this paper presents a method for the early identification and evaluation of unstable rock masses in high slopes using Unmanned Aerial Vehicle (UAV) digital photogrammetry and geographic information technology. By considering nine evaluation indices including geology, topography, and induced conditions within the study area, weights for each index are determined through an analytic hierarchy process. A semi-automatic approach is then utilized to extract and analyze rock mass stability. The reliability of this early identification method is confirmed by applying the limit equilibrium principle. The findings reveal that 17.6% of dangerous rock masses in the study area fall into the unstable category (W4, W6, W10). This method effectively assesses slope rock mass stability while providing technical support for disaster monitoring systems, warning mechanisms, and railway infrastructure safety defense capability to ensure safe mountain railway operations.

Wave dissipation effect of a new combined breakwater and its protective performance for coastal box girder bridges

Breakwaters play an important role in in mitigating wave-induced damage to marine structures. However, conventional submerged breakwaters often exhibit limited wave dissipation capabilities, while floating breakwaters may lack adequate safety performance. Therefore, this study introduces a novel combined breakwater design aimed at addressing the shortcomings of both traditional types. The proposed breakwater integrates a floating structure with a trapezoidal submerged breakwater via an anchor chain connection. To evaluate its efficacy, numerical simulations of wave interactions with structures were conducted using the OpenFOAM computational fluid dynamics (CFD) software in a two-dimensional (2D) numerical flume. Dynamic mesh technology was employed to simulate the motion of the floating body, and the resulting wave loads on a box girder bridge deck positioned behind the breakwater were analyzed to assess the combined breakwater’s protective capabilities and influencing factors. Analysis of wave heights and loads on the bridge deck revealed that the combined breakwater outperformed traditional submerged breakwaters in terms of wave dissipation. Furthermore, it was observed that the protective efficacy of the combined breakwater was more sensitive to variations in the size of the floating body compared to the submerged structure, and more responsive to changes in wave period than wave height. Leveraging the ability of the floating body to attenuate waves near the surface and the enhanced impact resistance provided by the combined floating and submerged structures, the proposed breakwater offers a promising approach to improving wave attenuation performance and enhancing safety for coastal infrastructure.

Detection of bridge damage through analysis of dynamic response to vehicular loads utilizing long-gauge sensors

PurposeThis paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.Design/methodology/approachThe method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.FindingsThis approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.Originality/valueOriginal research paper.

Enhancing Pedestrian Safety by Providing a LiDAR-Based Analysis of Jaywalking Conflicts at Signalized Intersections

Motives: In response to the inherent vulnerability of pedestrians in urban settings, this paper is driven by a commitment to enhancing their mobility and safety. Recognizing the prevalence of jaywalking as a significant concern, the study seeks practical solutions through the application of LiDAR sensors at signalized intersections. By delving into the complexities of jaywalking events and their contributing factors, the research aims to provide valuable insights that extend beyond mere statistical analysis. The motivations behind this endeavour lie in the imperative to comprehensively understand and address the risks associated with jaywalking, ultimately fostering a safer environment for pedestrians navigating urban crossroads. Aim: The primary aim of this paper is to assess and analyse the diverse factors influencing the frequency of jaywalking at signalized intersections, leveraging the capabilities of LiDAR sensors for safety applications. Through a meticulous examination of 1000 jaywalking events detected over a six-month period, the study aims to pinpoint highly correlated independent variables to the frequency of jaywalking events. These variables include traffic signal controller patterns, signal phases, vehicle-pedestrian conflicts, weather conditions, vehicle volume, walking patterns toward the median, pedestrian volume, and the unique jaywalker’s ratio. Employing advanced statistical regression models, the research seeks to identify optimal models and unravel key insights into the nuanced dynamics of jaywalking behaviour. The overarching goal is to equip decision-makers and transportation specialists with data-driven knowledge, enabling them to implement targeted safety measures that mitigate pedestrian risks and enhance safety infrastructure at critical urban crossroads. Results: The outcomes of the study, derived from the optimal Poisson regression model, yield crucial insights into the multifaceted nature of jaywalking events at signalized intersections. The morning and mid-day signal controller patterns exhibit a substantial decrease of 44.7% and 34.4%, respectively, compared to the evening (PM) pattern, shedding light on temporal nuances in jaywalking behaviour. Additionally, the severity of vehicle-pedestrian conflicts escalates proportionally with the number of jaywalkers, emphasizing the importance of addressing pedestrian flow in mitigating potential conflicts. Notably, the presence of vegetation in the median emerges as a significant factor, significantly increasing the frequency of jaywalking. These results contribute to a nuanced understanding of the intricate interplay between environmental, temporal, and behavioural factors in jaywalking incidents. Decision-makers and transportation specialists can leverage these findings to formulate targeted safety interventions, fostering a safer pedestrian experience at crucial urban crossroads

Modeling of impact of operations and maintenance on safety, availability, capacity, and cost of Railways-A System dynamics approach

the transport infrastructure, particularly the railway infrastructure plays a vital role in the delivery of freight and the transportation of people. The ability and reliability of the railway infrastructure to deliver goods and transport people are challenged by train derailments and collisions caused by infrastructure breakdowns. Lack of maintenance has been identified as one of the causes of infrastructure breakdowns leading to accidents. The current paper proposes that if the railway infrastructure safety, availability, capacity, and cost are modeled using system dynamics, the impact of infrastructure operation and maintenance on safety can be predicted more accurately. The paper follows systems thinking approach that aims to understand the railway infrastructure as a system, by defining the system structure, system component relationships, and system behavior. The impact on railway infrastructure is modeled using system dynamics by developing causal loop diagrams and stock and flow diagrams which define the system structure, and system component relationships, and models the system behavior of safety, availability, capacity, and cost.

Establishing resilience-targeted prediction models of rainfall for transportation infrastructures for three demonstration regions in China

Rainstorm is one of the global meteorological disasters that threaten the safety of transportation infrastructure and the connectivity of transportation system. Aiming to support the resilience assessment of transportation infrastructure in three representative regions: Sichuan–Chongqing, Yangtze River Delta, and Beijing-Tianjin-Hebei-Shandong, rainfall data over 40 years in the three regions are collected, and the temporal distribution of rainfall are analyzed. Prediction equations of rainfall are established. For the purpose of this, the probabilistic density function (PDF) is assigned to the rainfall by fitting the frequency distribution histogram. Using the assigned PDF, the rainfall data are transformed into standard normal space where regression of prediction equations is performed and the prediction accuracy is tested. The results show that: (1) The frequency of rainfall in the three regions follows a lognormal distribution based on which the prediction equations of rainfall can be established in standard normal space. The error of regression shows no remarkable dependence on self-variables, and the significance analysis indicates that the equations proposed in this paper are plausible for predicting rainfalls for the three regions. (2) The Yangtze River Delta region has a higher risk of rainstorm disaster compared to the other two regions according to the frequency of rainfall and the return period of precipitation concentration. (3) Over the period of 1980–2021, the Sichuan–Chongqing region witnessed an increase in yearly rainfall but a decrease in rainstorm disasters, whereas the other two regions experienced a consistent rise in both metrics.