A Probabilistic Decision-Making Framework for Coastal Railroads

Integrating Monte Carlo Weather Risk Simulation and Value Engineering for Marine Bridge Foundation Planning: Abu Qir Sea Bridge Case Study

Marine bridge foundations in shallow coastal waters are highly sensitive to weather‑induced stoppages, yet many projects are still planned using deterministic schedules with fixed allowances for seasonal storms (seasonal meteorological phenomena). This paper presents an integrated framework applied to the Abu Qir Sea Bridge in Alexandria, Egypt, where a three-year deterministic schedule with seasonal storm buffers proved insufficient to reliably account for weather‑induced stoppages at the critical axis 24 foundations, indicating a high risk of significant delay if executed as originally planned. Daily site records covering more than one year were used to quantify weather-related stoppages and to develop a 1,000-iteration Monte Carlo schedule risk model that converts observed downtime into realistic calendar‑based durations and probabilistic completion dates. These risk‑adjusted durations feed a value engineering study that evaluates three execution alternatives (Scenarios S2, S3 and S4) alongside the baseline marine method (Scenario S1). The results show that temporary reclamation and dry construction (Scenario S4) provide the shortest risk‑adjusted duration for axis 24 foundations, a removable caisson and sheet‑pile cofferdam solution (Scenario S3) minimizes total foundation cost for Pier P24, and marine piling with precast working slabs (Scenario S2) is preferred for smaller pier foundations. The proposed framework improves the planning of marine bridge foundations by defining more realistic and reliable schedules and by supporting the selection of suitable execution methods and better‑informed decisions under uncertain weather conditions.

Road asset exposure and risk assessment of extreme storm flooding under a changing climate: a case of Shanghai

A probabilistic state assessment framework for damaged heavy-haul railway bridges using vehicle-bridge collaborative monitoring.

In-service heavy-haul railway bridges often operate under damaged conditions, requiring continuous health monitoring to ensure operational safety and facilitate intelligent maintenance. However, conventional assessment methods based on ground monitoring, limited by sparse sensor deployment, cannot fully capture the global bridge state and its evolution, and neglect stochastic track irregularities that induce significant response variability, thereby decreasing assessment reliability. To address these issues, a distribution-driven probabilistic bridge state assessment framework is proposed based on vehicle-bridge collaborative monitoring. The extreme-value distributions of vibration responses from the bogie, wheelset, and key span sections under stochastic track irregularities and varying bridge damage conditions are characterized using the probability density evolution method, revealing a mapping between damage-induced variations in bridge states and the corresponding distribution shifts. Based on this mapping, baseline thresholds for extreme responses are statistically determined under undamaged conditions to define six distinct bridge health levels. The probability for each level is estimated via a weight-adaptive hierarchical probabilistic evaluation model, which fuses probabilities derived from the extreme-value distributions of multi-source vibration response indicators, with weights allocated according to indicator sensitivity to damage. The bridge state is assessed as the one with the highest probability among the six levels. Case studies on scenarios with different damage locations and severities demonstrate that the proposed framework effectively distinguishes the effects of damage on bridge states and traces state evolution as damage progresses, providing reliable and interpretable assessments for bridge maintenance decision-making.

Assessing and Optimizing Flood Recovery Strategies for Road Network Disruptions in Shenzhen Based on Isochrone Analysis

The escalating threats of climate change and rapid urbanization to urban sustainability have intensified the urgency for effective flood recovery strategies, particularly regarding critical infrastructure such as road networks. Shenzhen, a megacity frequently hit by short-duration heavy rainfall and typhoon-induced storms, faces high flood risk that often causes severe road network disruption. This study proposes an integrated approach bridging flood simulation, loss assessment, and isochrone-based accessibility analysis to evaluate and mitigate flood impacts across the road network of Shenzhen City under various rainfall return periods. The flood simulation combines the Soil Conservation Service Curve Number (SCS-CN) model for runoff estimation with a DEM-based water accumulation algorithm. The results demonstrate that: (1) Increasing rainfall return periods lead to a progressive expansion of inundation areas, predominantly affecting commercial, educational, and industrial sectors, while road network loss based on the inundation depths escalates rapidly under a 10-year return period rainfall and stabilizes beyond the 20-year threshold; (2) Isochrone analysis reveals that accessibility to emergency centers undergoes accelerated decay for rainfall return periods shorter than 20 years, with the most pronounced degradation observed along the 4- to 6-min isochrones; (3) Post-disaster recovery strategies prioritizing isochrone decay directions outperform those based on road hierarchy, particularly in the 4-min critical zones. This research provides robust analytical tools and insights for identifying vulnerable road sections and nodes during flood events, facilitating the prioritization of road network recovery.

Wind Damage vs. Storm Surge Damage: Case Studies from Hurricane Helene

Between 1980 and 2024, natural hazards have resulted in approximately $2.9 trillion in economic losses across the United States. Tropical cyclones represent the most damaging hazard type, accounting for approximately 53% of total losses, and are classified as multi-hazard events due to the combined impacts of wind loading and storm surge inundation. In the contiguous United States, coastal regions comprise only 10% of the total land area yet contain approximately 40% of the population, making these communities particularly susceptible to damage from hurricane-induced wind as well as storm surge-related forces. Following major events, post-disaster damage assessments conducted by federal agencies (such as FEMA) and by private-sector entities (including insurance carriers) are tasked with distinguishing between wind-related and storm surge-related damage. This forensic differentiation is critical for structural failure analysis, accurate insurance claims adjudication, and equitable allocation of recovery resources. Misattribution can lead to substantial disputes and financial discrepancies. This paper presents a case study for both pre-event vulnerability assessments and post-event forensic evaluations aimed at identifying and differentiating wind-induced versus storm surge-induced damage to residential buildings. The methodology described in this document integrates civil engineering principles and forensic investigation techniques to provide guidance for improving damage attribution accuracy and post-disaster decision-making.

Stochastic Modeling of Motorway Work Zone Capacity from Field Data in Slovenia

As Slovenia’s motorway infrastructure matures and approaches the end of its design life, maintenance-related work zones are becoming increasingly frequent, making the estimation of their capacity a crucial issue for effective traffic management. In addition to maintenance activities, the planned expansion of the motorway network with additional lanes is expected to introduce numerous large-scale construction work zones in the coming years. Accurate capacity assessment is essential for minimizing congestion, ensuring safety, and supporting maintenance scheduling and construction planning on heavily used motorway sections that experience temporary lane closures and geometric restrictions. In current practice, the Highway Capacity Manual and most empirical models provide deterministic capacity estimates, whereas stochastic approaches—particularly those based on the Weibull distribution—have been successfully applied to describe capacity reliability on basic freeway segments. However, such probabilistic concepts have not yet been systematically applied to motorway work zones, where lane reductions and geometric constraints substantially alter traffic dynamics. This study applies a stochastic modeling framework that integrates survival-based capacity sampling with Weibull distribution fitting to estimate motorway work zone capacity using detailed field data from the Slovenian motorway network. The analysis was performed on several work zone configurations for which sufficient prebreakdown flow observations were available to calibrate Weibull capacity distributions. The resulting distribution parameters provide probabilistic measures of breakdown likelihood and allow comparison of reliability levels between work zone configurations under different geometric and traffic conditions. Results show significant capacity reductions in work zones involving lane narrowing and crossovers, while the Weibull-based framework effectively captures the variability and reliability of capacity across different configurations. The study demonstrates the practical applicability of stochastic capacity modeling for motorway work zones and its value for data-driven planning, reliability assessment, and management of maintenance and expansion activities in mature motorway networks.