Approaches to urban heritage governance increasingly prioritise material form and quantifiable metrics, yet how measurable environmental quality relates to the public's evaluative-affective experience of heritage remains poorly understood. This study develops a perception-sensitive diagnostic framework that integrates deep learning on street-view imagery with Multiscale Geographically Weighted Regression to examine the spatial congruence and divergence between objective environmental indicators and computationally inferred perceptual responses. Applied to the Xi'an City Wall, the analysis reveals pronounced spatial heterogeneity and identifies two diagnostic zone types: resonance zones, where positive perceptual responses persist despite modest environmental quality, and discordance zones, where high environmental quality fails to elicit correspondingly positive scores. MGWR results show that the influence of factors such as road density and green-space coverage varies substantially in direction and magnitude across the study area, confirming that globally uniform assessment assumptions obscure locally important dynamics. By rendering these patterns visible, the framework contributes a scalable diagnostic layer to the perceptual mapping called for by the Historic Urban Landscape approach and provides empirical grounding for the argument that technocratic assessment may systematically differ from resident lived experience, supporting more perception-sensitive and spatially targeted heritage management.
Temporary traffic management (TTM) is transitioning from prescriptive standards to risk-informed approaches in New Zealand and other jurisdictions. Whether practices adopted under the 'risk-based' label align with established risk analysis and uncertainty characterisation literature has not been evaluated. This study addresses that gap through a systems-level documentary analysis. Regulatory, guidance, training, procurement, and academic documents are coded across a five-stage decision cycle and mapped against two benchmarks: Pat & eacute;-Cornell's uncertainty treatment levels and Aven's risk description framework (A ', C ', Q, K). The resulting maturity profile places New Zealand's TTM system at Levels 1-2 across all five stages. Hazards are identified from generic lists, consequences described with fixed categories, no explicit uncertainty measure accompanies any stage, and background knowledge is disconnected from risk descriptions. The system uses the language of risk-informed decision-making without producing the risk descriptions the benchmarks require. Improvement requires explicit risk descriptions proportionate to site exposure, complexity, and consequence - not necessarily advanced probabilistic methods. The benchmarking method is replicable and can be applied by other jurisdictions to their own TTM systems.
Infrastructures are sociotechnical systems embodying societal structures. In states characterised by colonial settlement, Indigenous peoples have often not benefited from infrastructure development. Here we show how historical and modern coastal transport infrastructure development in Aotearoa New Zealand impacted Indigenous landscapes of a M & amacr;ori subtribe in the Kaik & omacr;ura region. Early twentieth century road and rail development, operationalised through legislative mechanisms, disturbed landscape features and sites of spiritual and historical importance, reflecting a general disregard in wider New Zealand society for M & amacr;ori rights, concerns and values. Despite improvements in relationships between the state and M & amacr;ori since the late twentieth century, encapsulated in environmental and cultural heritage legislation, legislative responses and infrastructure reinstatement following the 2016 Hurunui/Kaik & omacr;ura Earthquake again led to the disturbance of Indigenous landscapes; inadequate community engagement and oversight resulted in further modification and destruction of cultural sites. This case study illustrates how engineering practice and infrastructure development are mediated by state power and legislation. While the engineering profession is now better integrating M & amacr;ori rights and concerns, and taking more responsibility for informed decision-making in ethically complex situations, how this may be operationalised after future disaster events for infrastructure recovery while minimising avoidable disturbance to Indigenous landscapes is still an open question.
Cascade dam groups are critical to water resource management but face complex, interconnected risks due to hydraulic interactions and structural dependencies. Traditional risk assessment methods often overlook the transfer effects of risks within such systems, leading to incomplete safety evaluations. This study develops a comprehensive risk transfer evaluation indicator system that incorporates inherent resistance characteristics and hydraulic risk transfer effects. The relative importance of each indicator is determined by combining ranking relation analysis (G1) and entropy weighting methods. A risk transfer model is then devised, and the risk transfer coefficient is quantified using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). To identify the most hazardous failure path, the minimum system reliability criterion is introduced, employing structural non-probabilistic reliability analysis theory optimised via the Harris Hawk optimisation (HHO) algorithm. A case study of a three-dam system demonstrates the method's effectiveness, identifying the path from Dam B to Dam A as the most critical. This approach provides technical support for the failure risk assessment of cascade dam groups and offers key insights for prioritising safety interventions.
Interdependencies among infrastructure systems may amplify the impact of disruptive events. This paper presents a network-based repair sequencing and resilience assessment model to examine the influence of two-way interdependency between road transportation networks and power distribution networks. We use a modified IEEE 33-bus power network and the Sioux Falls road network (both standard testbed instances) to simulate the effects of a natural disaster, with a single repair crew repairing damaged nodes. Power failures cause delays in the transportation system due to signal outages, and delays in the transportation system affect the times when the repair crew can reach damaged nodes. We use a simulated annealing algorithm heuristic to find good repair sequences that account for both types of interactions between power and road systems. We compare the solutions obtained from the heuristic algorithm to alternative strategies and results indicate that the interdependency-aware strategy achieves faster restoration times and enhanced overall resilience, compared to random, priority-based, and interdependency-naive strategies.
As global climate change and infrastructure ageing accelerate, particularly in coastal regions, the demand for real-time life-cycle seismic management of bridges and rapid post-earthquake response is increasing. Traditional seismic fragility assessments, based on static models or finite element (FE) analyses at specific time points, are computationally intensive and inefficient, and fail to meet practical decision-making needs. This study proposes a real-time life-cycle (0-100 years) seismic fragility assessment framework for coastal bridges, accounting for time-dependent deterioration caused by chloride corrosion, with a case study of medium- and small-span curved bridges in southeastern coastal China. A corrosion-informed numerical model is developed, and parametric cyclic FE analyses generate seismic fragilities at different service years, forming time-series samples of fragility parameters. Based on these samples, a deep temporal neural network (DTNN) predicts time-varying fragility parameters, and a graphical user interface (GUI) is developed. The results demonstrate that seismic fragility increases with service time and is affected by horizontal curvature. The proposed model accurately captures long-term fragility evolution, achieving a mean absolute percentage error below 5%. The GUI produces fragility curves for any service year within 11.4 seconds, reducing computational time by over 1500 times compared with conventional FE-based methods.
Urban multimodal transportation networks are crucial for social and economic development but remain highly vulnerable to natural hazards. Existing research has primarily focused on single-mode systems, overlooking intermodal interdependencies that shape passenger flow redistribution and can trigger cascading failures. This study proposes a model framework for assessing the vulnerability of multimodal public transportation networks by combining geospatial data with network science-based methods. A bidirectional subway-tram network is constructed for Amsterdam to validate the feasibility, where disruption scenarios are simulated combining percolation analysis and connectivity loss metrics. The results show that intermodal transfer links substantially improve network redundancy and that neglecting them in simulations leads to an overestimation of connectivity losses. Critical links and stations without sufficient alternatives are identified as key vulnerabilities under disruptions. Percolation analysis further captures degradation processes from minor to large-scale failures, offering a more comprehensive perspective than single-event simulations. Overall, the study demonstrates the feasibility of geospatial-based multi-layer network analysis as a decision-support tool for enhancing the resilience of urban multimodal public transportation systems. Moreover, this framework can be extended to incorporate additional modes and passenger demand data in future applications.
Indian tunnel projects often face delays that inflate budgets, disrupt timelines, and threaten project success. This study introduces a novel activity-centric, scenario-based risk analysis framework to forecast and mitigate delays by combining risk parameters: probability, exposure, and consequence. Delay predictions are made by analysing risk scenarios probability-exposure (Scenario 1), exposure-consequence (Scenario 2), and probability-consequence (Scenario 3). The methodology starts by linking 16 most significant risk factors to tunnel activities. Activity-wise risk levels are computed using an AHP-driven Event Tree Analysis (ETA) to evaluate consequences and Fault Tree Analysis (FTA) to assess probability and exposure. These levels are integrated into the project schedule to forecast delays. Methodology enables systematic identification, prioritisation, and tracking of cascading risk impacts at the activity level. Drawing real-time data from Indian metro tunnel projects, the study reveals distinct risk scenarios are more effective for the planning and execution phases. Scenario 3 (exposure-consequence) closely aligns with delays in planning, while scenarios 1 and 2 perform better for execution. Findings reveal that major delay-sensitive activities include NATM excavations, TBM dragging, method-statement preparation, and soil investigation. The study also introduces an activity-based risk matrix with tailored mitigation strategies, strengthening schedule resilience and enabling project managers for timely, risk-informed project delivery.
Owners commonly request that contractors provide performance bonds as financial security during a project's execution. While these bonds mitigate owner risk, they financially burden contractors. Consequently, the costs of performance bonds are factored into a contractor's bid evaluation. The implementation of reasonable bond requirements can encourage greater contractor participation in project bidding, thereby increasing competition and potentially lowering project contract prices for owners. However, it is challenging to properly balance protecting owners' interests against maintaining contractors' willingness to bid. This study aims to propose an integrated discrete event simulation model to address the key challenge of how to achieve the best balance which is approached from the perspective of a single owner's internal cash flow - risk. The model quantifies the cash flow and financial burden of construction projects and related companies under overlapping project scenarios, incorporating various random uncertainties to predict the likelihood of contractors successfully managing cash flow and completing projects. These simulation results will be directly used to assess contractors' willingness to bid under different performance bond scenarios, thereby providing owners with specific and quantitative decision support to help set a performance bond percentage that protects their own interests while promoting healthy market competition.
This study presents an improved risk assessment framework for composite Lenj vessels by integrating Failure Mode and Effects Analysis (FMEA) with CRiteria Importance Through Intercriteria Correlation (CRITIC) and COmbinative Distance-based ASsessment (CODAS) methods. While the hybrid approach overcomes key limitations of traditional FMEA, such as its inability to differentiate failure modes with identical Risk Priority Numbers, the true value of this work lies in its comprehensive risk assessment capability. By combining dual-distance evaluation with weighting that accounts for the statistical variability and inter-criteria conflict within the data itself, the framework provides actionable insights that can directly support decision-making in maritime safety management. Validation shows strong reliability (alpha = 0.85) and robustness across parameter variations. The method offers practical benefits, including targeted maintenance strategies, improved quality control in vessel manufacturing, and enhanced operational safety. Ultimately, this work illustrates how integrating objective analytical techniques transforms FMEA into a practical decision-support framework- empowering marine stakeholders to prioritise actions, allocate resources, and enhance operational safety with confidence. The generalisable nature of this methodology suggests its high potential for application in other domains, such as civil engineering, to assess and mitigate risks in infrastructure projects.
Modern societies depend on critical infrastructure for resilience against attacks and hazardous events, with underground spaces offering defensive advantages. This paper provides a system-level analysis of the Gaza Tunnel Network (GTN), highlighting its resilience under severe attacks. We discuss the engineering evolution of this underground system, which consists of shafts, tunnels, rooms, and large caverns. Key aspects of the GTN's durability, versatility, redundancy, and recoverability are examined, with implications for contemporary design policies for planning underground spaces for both peacetime and defense functions. The resilience of the GTN demonstrates that underground structures may withstand threats that are conventionally assumed to cause failure. Recognising this may justify reducing design conservatism, thereby enabling the construction of larger public shelters within the same resource constraints.
Earthquakes can damage enterprise housing and infrastructure, resulting in production shutdown and potential losses, especially in the urban consumer service industry. Therefore, in this study, we propose a method to assess the seismic resilience of the urban consumer service industry based on existing resilience frameworks. This method evaluates the impact of earthquakes and its recovery characteristics in terms of the ground motion intensity, damage state of industry premises , industry type, and regional characteristics, considering both time and financial loss dimensions. Based on field investigationsas well as expert experience, we presented a method to calculate the recovery time of hotel and catering enterprises. Based on the statistical results, the seismic resilience grading standards were developed for the enterprises, and eight resilience enhancement measures and their effects were provided. Based on this framework, the seismic resilience of the enterprises in Moxi Town was evaluated under the Luding earthquake scenario. The proposed calculation method yielded results that were in good agreement with the actual values, which can reflect the seismic resilience of urban consumer service enterprises and facilitate the government and enterprises in reducing the impact of earthquakes by pre-earthquake planning and post-earthquake emergency responses.
International transportation projects (ITPs) play an important role in eliminating cross-border and regional transportation bottlenecks, and the development of global trade. The ITPs face high uncertainties due to the dynamic external environment and the complexity of international stakeholders, hence are more often experiencing suspensions and cancellations during the whole project lifecycle from development and design to construction and operation. However, there is currently a lack of systematic analysis regarding the discontinuation of ITP lifecycle. This study adopts a case data mining method to analyze the discontinuation of ITPs and the impact factors from a systematic view of whole life cycle (WLC) perspective. The results reveal the dynamics of the impact factors for project suspension and cancellation. The project type and regional analysis reveal distinguished distributions of the key impact factors. The cognitive mapping of stakeholders discovers that the local government is the primary initiator of suspension and cancellation, and the foreign policy banks and host government institutions are the recipients of the negative consequences. Suggestions are provided to practitioners in civil engineering and researchers in ITPs to help better understand and systematically eliminate the discontinuation of the projects.
Despite significant efforts made to address safety issues, the construction industry still faces high rates of fall-related injuries and fatalities. Meanwhile, Building Information Modeling (BIM), which is increasingly being adopted, offers a proactive approach to safety management by enabling detailed hazard visualisation and analysis during design. Accordingly, this study proposes a novel BIM-based method for the automatic detection, assessment, and mitigation of fall risks in building projects. This method automatically identifies fall hazard areas, computes Risk Priority Numbers (RPNs) based on likelihood and consequence, and visualises risks within the BIM environment, thereby facilitating targeted mitigation. To evaluate the proposed method, a case study is conducted comparing this approach with traditional 2D drawings and 3D Revit models. While experts detected 81-93% of risks, assessed 46-67%, and mitigated 31-38% of risks in the 2D drawings and 3D Revit models, the proposed approach identified, assessed, and facilitated the design of mitigation approaches for all detected fall risks, significantly reducing the required time. Therefore, the findings confirm the effectiveness of the proposed method. This integrated BIM-based approach offers construction professionals a proactive tool for fall hazard management, enhancing safety planning and promoting a safer work environment.
This study proposes an adaptive decision making approach integrating the Delphi method and compromise-based multicriteria decision making (MCDM) techniques to evaluate reservoir sediment reuse on offshore islands. By forecasting experts' scores, an adaptive decision matrix is established to reduce decision making risk. A case study of Kinmen Island demonstrates the approach, examining local sediment characteristics and social demands. The Delphi method facilitates expert consensus, while the compromise-based MCDM framework aligns decisions with expert preferences. The study identifies solutions such as historic building restoration and pavement backfill as particularly suitable for Kinmen Island, reflecting its cultural and economic context. The results underscore the importance of considering the life cycle of alternatives and demonstrate the value of adaptive decision making models for promoting offshore island sustainability. This low-risk, adaptable approach supports long-term sustainability by enhancing reliability, reducing bias, and minimising conflicts of interest through expert consensus. It dynamically forecasts evaluations and conditions, increasing flexibility and adaptability. By integrating life cycle considerations with local industrial development, the approach addresses policy changes and resource constraints effectively. These findings contribute to a broader understanding of engineering decision making in regions facing rapid technological changes and significant environmental challenges.
