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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Probabilistic seismic demand of typical highway bridges in moderate-intensity zones</title>
      <link>https://trid.trb.org/View/2720338</link>
      <description><![CDATA[The probabilistic seismic demand for typical highway bridges in moderate-intensity zones is investigated. The seismic demands and infrastructure destructions are severe on critical bridge components during and after seismic activity in moderate-intensity zones. This article aims to determine seismic demand, predict the probabilistic seismic demand model (PSDM), and identify the interdependence of bridge components. Based on bin-ground motion, 154 analytical bridge-ground motion samples are prepared to record output data using nonlinear time history analysis (NLTHA). High-level ground motion duration and peak ground acceleration are used to measure the seismic demand and intensity of the bridge. The best-fitting lognormal distribution and correlation coefficient between demand in the transformation states are analysed to indicate the interaction between the two at dissimilar levels of interdependence. As a result, bearing pads on all abutments, the bearing pad on pier-1, and the shear key on pier-2 are best fitted with a high level of interdependence to design the seismic performance of highway bridges. Therefore, the ground motion characteristics play a vital role in the infrastructure collapse of the highway bridges in the moderate-intensity zone and should be held in high regard.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720338</guid>
    </item>
    <item>
      <title>Combining long short-term memory and shape superposition for estimating moving load-induced global responses of bridges</title>
      <link>https://trid.trb.org/View/2720337</link>
      <description><![CDATA[Recently, a method of estimating the global responses of bridges was introduced, by combining a deep neural network (DNN) and shape superposition method (SSM). This DNN-SSM model demonstrated superior performance to the traditional estimation method based on the least-squares method. However, the application and validation of this approach have been restricted to static problems. For bridges, the dynamic effect induced by the moving load requires consideration. Therefore, this study proposes a long short-term memory (LSTM)-SSM model to improve the estimation performance for dynamic global responses. The LSTM-SSM model consists of LSTM layers, followed by fully connected DNN layers and a post-process part based on SSM. Limited displacement, slope, and strain data were used as the inputs, and the global displacement, slope, and strain were estimated. To validate the effectiveness and improvement of the LSTM-SSM model, a numerical model of a cable-stayed bridge was used to compare the proposed model with the previously developed DNN-SSM model. The validation results indicated that the LSTM-SSM model can provide more accurate estimates of the dynamic global response than DNN-SSM models. Finally, a technique for structural safety monitoring based on the proposed method was introduced.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720337</guid>
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    <item>
      <title>Surface damage detection of cable stays based on PointRend model using unmanned aerial vehicles</title>
      <link>https://trid.trb.org/View/2720333</link>
      <description><![CDATA[Cable stays are critical load-bearing components that are significant of cable-stayed bridges. Traditional detection methods have several shortcomings. To address these problems, this study proposes a detection method that uses unmanned aerial vehicles to shoot videos of cable stays and identifies surface damage through deep learning. To improve the robustness of the method for detecting cable damage, the proposed method consists of three phases: background removal, damage recognition, and planar unfolding. In the first phase, a Background Removal Model based on PointRend is used to remove complex backgrounds of cable images, which could also reduce computational costs for subsequent processing of non-damage images. In the second phase, a Damage Recognition Model based on PointRend performs pixel-level semantic segmentation of damage. In the third phase, cable surface images are unfolded to eliminate the image distortion. On a self-made dataset, the proposed method achieved a mIoU score of 89.90%. Experimental results demonstrate the effectiveness of the proposed method in detecting cable stays’ surface damage.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720333</guid>
    </item>
    <item>
      <title>Transfer from defective bridge information model to defect analysis model based on industry foundation classes</title>
      <link>https://trid.trb.org/View/2720332</link>
      <description><![CDATA[Bridges, as an important part in modern infrastructure networks, suffer from increasingly severe aging issues in recent years. Road agencies and authorities conduct regular inspections for their bridges and identify defects and deteriorations for condition assessment. Such defect information cannot be efficiently used for analysis in the structural domain. This study proposes an Industry Foundation Classes-based method to allow for the transfer from a defective bridge information model to a defect analysis model. The architectural model of bridge is first converted into analytical model and then the impacts of defects on structural properties are quantified with a stiffness reduction coefficient. Case studies are conducted to validate the applicability of the proposed method for defect analysis and a range of coefficients are tested on an illustrative case bridge. The results prove that the proposed method can generate instructive and informative knowledge on the safety and reliability of the entire structure. This study narrows the gap between the empirical condition assessment scheme and the numerical structural analysis scheme for bridge management and manages to make full use of inspection-related information within Building Information Modelling (BIM) environment.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720332</guid>
    </item>
    <item>
      <title>Framework for rational decision-making in bridge maintenance</title>
      <link>https://trid.trb.org/View/2720330</link>
      <description><![CDATA[Bridges are deteriorating, and their safety must be continuously assessed. Efficient management of bridges is essentially about making decisions on maintenance activities in line with the decision maker’s preferences. In other words, structural safety should be ensured while optimally allocating resources. In theory, methods are available for solving this complex task; however, they have not yet been implemented by bridge owners. In this study, the implementation into practice is facilitated by presenting a consistent framework for rational decision-making. It is argued that structural assessment is a decision problem that infrastructure owners are obliged to take responsibility for. Bayesian decision theory is presented as the theoretically ideal decision-making framework. The main components of the framework—representations of structural reliability and failure consequences—are discussed in detail, and human safety constraints are integrated. For demonstration purposes, the decision-making framework is applied to two case studies. In addition, it is parameterised by the relative cost of the intervention to failure and the current failure probability to be applicable for any assessment situation. In that way, this study contributes to more efficient maintenance of single bridges and the development of generally applicable structural codes for assessing existing structures.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720330</guid>
    </item>
    <item>
      <title>A systematic risk assessment approach for urban roadside infrastructure assets</title>
      <link>https://trid.trb.org/View/2720329</link>
      <description><![CDATA[Revealing potential risks and the influence of these risks on urban roadside vulnerable/risky zones has been a great deal for the last decades owing to increasing vehicular mobility. This study aimed to develop a risk assessment methodology for urban roadside infrastructure assets. In this context, seven risk parameters that are likely to affect hazardous aboveground assets installed on urban roadsides were determined, and weights of each parameter (order of importance) were presented based on expert surveys and literature information using the Analytic Hierarchy Process (AHP) method. Mathematical models were set up for each risk parameter using real field data, The American Association of State Highway and Transportation Officials (AASHTO), literature, related standards, and expert surveys by linear, nonlinear, and binary logistic regression analyses. Risk groups were created, and precautions were offered. The efficiency of the models was verified based on the 28 accident records and field observations in 172 assets. When considering the assets that undergo traffic accidents, 71% of the total assets are ranked in the critical and high-risk group. This ratio is consistent with the risk group definitions assigned in this study; thus, the model proposed is accurate and can be applied reliably. The verification process of the total risk model was sufficiently successful to be used in practice and can be generalized by considering the road and traffic characteristics, infrastructure facilities, and future requirements of the regions or countries that reflect similar historical and cultural concerns. Practitioners, governmental institutes, and researchers can apply this methodology to provide their own data with high transparency and reliability.]]></description>
      <pubDate>Thu, 16 Jul 2026 09:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720329</guid>
    </item>
    <item>
      <title>Life-cycle multi-criteria decision making of bridge selections considering uncertainty under earthquakes and deterioration</title>
      <link>https://trid.trb.org/View/2708293</link>
      <description><![CDATA[This study proposes a life-cycle multi-criteria decision-making (MCDM) framework for bridge selection under uncertainty, considering the combined effects of earthquakes and deterioration. The framework evaluates multiple criteria, including life-cycle sustainability, resilience, construction cost, carbon dioxide emissions, and application difficulty. Various sources such as structural deterioration and seismic hazards introduce uncertainties that influence bridges’ long-term sustainability and resilience. To address these challenges, this study integrates the TOPSIS–SMAA algorithm, which combines the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking alternatives and Stochastic Multi-Criteria Acceptability Analysis (SMAA) for handling uncertainty in criteria and decision preferences. The developed approach is applied to the decision making of an SMA–steel reinforced bridge. The results indicate that while the SMA–steel reinforced bridge exhibits superior life-cycle sustainability and resilience due to its self-centering capability and corrosion resistance, it also presents challenges such as higher construction costs, increased construction carbon emissions, and greater implementation complexity. The TOPSIS–SMAA method effectively addresses uncertainties in multiple criteria and decision-maker preferences. The holistic acceptability index reveals that the novel bridge is a more favourable choice in a life-cycle context. The proposed framework provides a robust decision-support tool for sustainable and resilient bridge infrastructure planning.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708293</guid>
    </item>
    <item>
      <title>Vibration-based damage detection and fatigue assessment in bridge cables</title>
      <link>https://trid.trb.org/View/2708292</link>
      <description><![CDATA[Cable safety evaluation is crucial for cable-supported bridges in service. Due to its cost-effectiveness and convenience, vibration-based methods are widely used in cable monitoring. The damage detection and fatigue assessment of the cable using vibration data are developed in this paper. Firstly, a combination method of the FFT and block recursive amplitude and phase estimation is proposed to quickly and accurately track time-varying frequency based on acceleration data. Secondly, the time-varying cable frequency is applied to detect damage and identify the time-varying cable force, respectively. For damage detection, a combined method of the moving average and empirical mode decomposition is used to remove the effect of traffic and temperature in time-varying frequency, then a K-means clustering method is used for quantitative identification of cable damage. For the identification of time-varying cable force, a fast and accurate cable force formula is fitted based on the cable inverse analysis characteristic function. Finally, a scheme of real-time monitoring and sensing of cable fatigue based on the detected damage and identified time-varying cable forces and Miner linear fatigue damage accumulation theory is proposed. This method considers both the real stress amplitude spectrum of the cable and the change in the mechanical properties of cables simultaneously.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708292</guid>
    </item>
    <item>
      <title>Non-stationary cellular automata for life-cycle assessment of concrete bridges under corrosion and climate change</title>
      <link>https://trid.trb.org/View/2708291</link>
      <description><![CDATA[The accuracy of life-cycle assessment of concrete structures exposed to corrosion due to ingress of chlorides and other aggressive agents can depend significantly on the diffusion models adopted. In addition, daily weather fluctuations and long-term changes in climatic conditions can exacerbate the effects of chloride ingress in concrete, accelerating the onset of steel reinforcement corrosion and increasing the rate of damage. To face this problem, a novel approach to chloride diffusion simulation and corrosion modelling to investigate the life-cycle structural performance under climate change using non-stationary cellular automata is presented. The proposed model is based on the extension of a general formulation established in previous works for life-cycle assessment of concrete structures under corrosion by adapting cellular automata to account for the evolution over time of the environmental parameters that influence the initiation and propagation of corrosion, i.e. temperature and relative humidity, in a changing climate. The methodology is applied to life-cycle reliability assessment of a bridge pier under corrosion considering different climate change scenarios. The results allow to quantify the impact of climate change on chloride diffusion, corrosion damage, and structural performance over time.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708291</guid>
    </item>
    <item>
      <title>Danish concrete bridge in-situ load testing: considerations and test results</title>
      <link>https://trid.trb.org/View/2708290</link>
      <description><![CDATA[A Danish concrete bridge proof loading project (V1) was initiated in 2016, followed by a project extension (V2) in 2023. The primary objective of V1 was to establish a classification-based proof loading procedure utilising the Danish classification system. In the V1 project, a multidisciplinary approach was developed for the evaluations involving multi-scale testing, theoretical response evaluations, and probabilistic assessments. Several bridges were successfully reclassified to a higher capacity through pilot projects using the developed procedure. Based on the findings, a Danish national guideline was published in December 2024. The V2 project builds on this foundation, expanding its scope to include multi-span and non-shear-reinforced concrete slab bridges. This paper presents key findings and milestones from the V1 project, encompassing the background, design, and execution of the first V2 in-situ tests on a Danish highway bridge, with representative results on non-shear reinforced wings and slabs. Shear failure in the concrete slab occurred at an axle load of approximately 250 tonnes. Shear failure initiation in the tested wing was reached at a wheel load of approximately 50 tonnes. The margin from crack identification to failure is currently under investigation, and extensive information is being generated to support a multidisciplinary approach, thus aiming to provide a basis for shear-related stop criteria.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708290</guid>
    </item>
    <item>
      <title>Seismic retrofit optimisation of multiple bridges using real options analysis</title>
      <link>https://trid.trb.org/View/2708289</link>
      <description><![CDATA[Maintaining bridge functionality requires timely retrofits to address both corrosion resistance and seismic performance. In general, retrofit planning may involve multiple bridges within a given region under the oversight of a bridge management agency. Therefore, retrofit strategies should be efficiently planned for multiple bridges in a network. This study introduces an approach for optimal seismic bridge column retrofit planning of multiple bridges in a bridge network. The optimisation is based on maximising the real option value (ROV). Real options analysis is conducted through cost-benefit assessments (CBA) of individual bridges in a bridge network. The CBA integrates time-variant fragility analyses and risk assessments for all individual bridges in a network, both with and without retrofitting, into a cumulative benefit. To efficiently address the time-dependent fragility of all individual bridges in a network, machine learning is utilised. ROVs for individual bridges are determined based on cumulative benefits. Aggregating the cumulative benefits of individual bridges estimates the ROVs for groups of bridges. Using the proposed approach, the necessity and optimal timing for bridge column retrofits can be determined for individual bridges, bridge groups, and the overall bridge network. The proposed approach is illustrated using existing bridges in a network located in South Korea.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708289</guid>
    </item>
    <item>
      <title>Improving life-cycle performance of degrading bridges under floods through retrofit measures</title>
      <link>https://trid.trb.org/View/2708288</link>
      <description><![CDATA[Global bridge failure statistics indicate that hydraulic actions, particularly hydrodynamic forces and scour, play major roles in bridge failure during floods. The scenario is specifically critical for ageing bridges in harsh environments. Because of continuous exposure to such environments, RC bridges with time become more susceptible to load demands during flood events. Hence, appropriate retrofit strategies are essential that can be implemented at any instance during service lives of bridges. This study identifies potential flood retrofit strategies for bridges and develops a framework to measure their effectiveness by analysing a simply supported RC riverine bridge before and after retrofit. As the bridge experiences scour as well, opted retrofit strategies include superstructure and scour retrofit. Computational fluid dynamics simulations are performed to calculate hydrodynamic forces on bridge superstructure, whereas code provisions are followed to calculate the same for piers. Finite element analysis of the bridge at various life-cycle years under different combinations of inundation ratio and flood velocity found that the ageing bridge, without and with scour retrofit, fails to sustain during extreme floods; yet, it becomes safe when superstructure retrofit strategies are implemented. Thus, obtained results demonstrate the effectiveness of retrofit strategies in mitigating flood risk of ageing bridges.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708288</guid>
    </item>
    <item>
      <title>Improving the decision-making and planning of future railway bridge interventions through digitalisation</title>
      <link>https://trid.trb.org/View/2708287</link>
      <description><![CDATA[Railway bridge managers estimate the intervention requirements years in advance, which include their associated costs, required track possession times to execute interventions, and failure risks. They communicate this information to multiple stakeholders involved in the intervention planning process using reports and tables. As it is difficult for stakeholders to process all the information in short periods of time this process can lead to misinterpretations, which in turn can lead to multiple iterations and discussions. With the rise of predictive algorithms and building information models (BIMs) to predict, plan, and manage future interventions, there is now an opportunity to use these tools to improve the efficiency of the planning process. This work presents a methodology to do this, i.e., to demonstrate how predictive algorithms can be connected to BIM to facilitate discussions of the multiple stakeholders involved in the intervention planning process, and how the process can be improved. The methodology is demonstrated on a 25 km railway network in Switzerland consisting of 30 bridges.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708287</guid>
    </item>
    <item>
      <title>Multi-objective resilience-based optimal prioritisation of ageing bridge networks under concurrent multiple hazards in a changing climate</title>
      <link>https://trid.trb.org/View/2708286</link>
      <description><![CDATA[Bridges are critical components of transportation networks and are highly vulnerable to various hazards throughout their lifespan. Natural disasters such as earthquake sequences and riverine floods create serious risks, while climate change is making these threats even more severe. In fact, climate change is increasing the frequency and intensity of extreme weather events, leading to more severe flooding and accelerated deterioration of structures, with a detrimental impact on the life-cycle performance and resilience of bridges and bridge networks. It is therefore essential to ensure adequate pre- and post-event functionality, particularly for traffic flow and road connectivity. To this purpose, rational prioritisation of rehabilitation activities is key for optimising limited resources and reducing both direct and indirect economic losses. This paper presents a probabilistic, resilience-based framework aimed at prioritising retrofit and restoration interventions on ageing bridges and bridge networks facing multiple hazards, including seismic mainshock and aftershocks, flooding, and corrosion under climate change. The proposed approach employs multi-objective optimisation to identify the most effective pre- and post- event activities that enhance resilience while minimising both direct and indirect costs. The application to a bridge transportation network illustrates how the proposed framework allows to addressing strategic rehabilitation planning and improving resilience while ensuring cost-efficiency.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708286</guid>
    </item>
    <item>
      <title>Sustainability and digitalisation in bridge management: present and future</title>
      <link>https://trid.trb.org/View/2708285</link>
      <description><![CDATA[Sustainability and digitalisation are two main topics in many research programs in Europe and worldwide. It seems that bridge managers can largely benefit from the current digital transformation where processes using digital technologies are able to create new or to enhance existing bridge management systems (BMS). The concepts of sustainability and digitalisation are very different in essence. The former is a societal need, while the later is just a tool. Therefore, the correct approach for their fully adoption in the bridge management environment has to be also different in their basis, formulation and implementation. For an optimal adoption and development of both topics, also the actual trends in the bridge management policies, as performance-based decision making and adaptive BMS should be considered. This paper analyzes the actual bridge management policies, trying to evaluate their current level of sustainability and digitalisation and what are the future steps to follow to achieve an optimal situation. It is shown that although in the technical aspects the goals are not so far, the human factor (educational and organisational aspects) remains as the critical factor for a full and optimal implementation.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708285</guid>
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