Bridge Case Study Research Articles

ANALITYCAL FIRE FRAGILITY ASSESSMENT FOR BRIDGES CONSIDERING FIRE SCENARIOS VARIABILITY

The Italian infrastructure network of roads and bridges is one of the most complex in the world, due to the orography of the territory. Italy is strongly interested by seismic and hydrogeological hazards and, in addition, degradation and obsolescence phenomena are common on infrastructures approaching the end of their nominal life. Furthermore, during their service life these infrastructures can be subjected also to extreme actions such as the fire. In particular, the past examples of damages on infrastructures as consequence of fire event and the high dependency on uncertain factors of fire have motivated the study for developing models to estimate the possible consequences that this phenomenon can cause on infrastructures. In particular, the paper sets a methodology to assess the structural fire fragility of infrastructures, based on fire hazard scenarios, developed specifically for bridges. Then, two existing pilot case studies (a reinforced concrete bridge and a composite steel-concrete one) are modelled, by deriving the structural response under different natural fire scenarios. By means of Cloud Analysis, a linear regression about the key parameters in the fire action definition and the structural response is assessed, by considering different performance levels, specifically proposed for bridges. Finally, the fragility curves are obtained, measuring the capacity of the case study bridges under the selected fire scenarios. The study also shows which of the fire scenarios parameters can be better correlated to the structural response of the bridge, that is useful for a proper fire resistance assessment and design.

Seismic resilience of deteriorating bridges under changing climatic conditions

This study underscores the critical need to integrate changing climatic conditions into corrosion models for civil engineering infrastructures, particularly highway bridges, given the potential reduction in structural performance post-seismic events. The paper introduces a novel framework for assessing the seismic resilience of deteriorated highway bridges in the context of changing climatic conditions. The framework is demonstrated on a non-seismically designed simply supported highway bridge situated near the sea in a seismically active region of Gujarat, India. An improved corrosion deterioration model is used that considers the impact of climate change and non-uniform pitting corrosion for evaluating the deterioration of RC bridge components. A detailed three-dimensional finite-element model of the case-study bridge is developed that can accurately simulate various failure modes of corroding bridge piers. Time-varying seismic fragility curves are developed using damage limit states and probabilistic seismic demand models while considering the influence of climate change. Bridge seismic resilience is estimated by aggregating the seismic vulnerability, losses, and recovery functions. Results show that incorporation of changing climatic factors will considerably reduce the seismic resilience of the 75-year corroded bridge up to 56 %. Finally, a comparison of seismic fragility and resilience is carried out using the proposed and conventional corrosion deterioration model to evaluate the significance of considering the effects of climate change in the seismic resilience assessment framework.

Case study of adjacent integral bridges supported on continuous secant piled walls

Integral bridges are often the preferred way of construction for short and medium span bridges in the UK. However, the behaviour of integral bridges is influenced by complex soil-structure interaction behaviour that may be cumbersome to predict and analyse when dealing with unusual structural arrangements. This paper presents a case study for the design of two single span bridges carrying a roundabout over a dual carriageway, supported on continuous secant piled walls. One of the bridges is fully integral while the adjacent structure is articulated at one end due to its skew. An alternative detail for the articulated end is proposed, to eliminate soil pressures and settlements at the back of the abutments as well as reduce the risk of water ingress to the bearings. The complex nature of the structural arrangement presented analytical challenges, that were solved by developing an advanced modelling approach, exploring two proposals. The first is to evaluate the geotechnical model for a range of imposed top of abutment displacements from the onset. The second, using unique non-linear springs and derived P-Y curves describes the more realistic variable nature of soil-structure interaction over the length of the walls leading to reduced soil pressures enhancing design feasibility.

Rapid Assessment Method of Buckling Stability of Bridge Pier–Cap–Pile System Under Scour Effect

A novel analytical method for evaluating the buckling stability of the pier–cap–pile system under scour effect is proposed, and furthermore, the concept of rapid assessment of bridge buckling stability based on natural frequency is innovatively introduced in this paper. First, the total potential energy function of the bridge pier–cap–pile system is established, and the closed-form solution of structural system critical buckling load under scouring is solved according to the principle of stationary potential energy. Second, the sensitivity analyses of pier height, pier width, bending stiffness reduction in pier, pile slenderness ratio, pile bending stiffness reduction, m-value of soil and structural gravity to the buckling load are carried out. On this basis, 60 sets of sensitive parameter sample combinations are extracted by the Latin Hypercube Sampling algorithm, and a proxy model relating the critical buckling load to scour depth and the sensitive parameters are then developed using the McQuarter global optimization algorithm. Finally, the regression relationship model between the critical buckling load and the natural frequency is formed by combining the proxy model and the existing relationship between the scour depth and the first-order natural frequency. The feasibility of the proposed assessment method is demonstrated by the finite element method using a bridge case study, which provides an insight and a new means of quantitatively evaluating the safety of bridges under scour effect.

Parameterized fragility-based uncertainty influence quantification and sensitivity analysis methodology: Concept, formulation, and application

Uncertainty modeling is a crucial issue in the seismic risk and resilience assessment of structures and infrastructures. Understanding and quantifying the impact of structural model parameter uncertainty (i.e., the epistemic uncertainty) on seismic fragility of structures are still open issues in the field of earthquake engineering. To this end, this study proposes a parameterized fragility-based methodology to quantify and rank the influence of multiple model parameter uncertainties on the seismic fragility result when their uncertainty is simultaneously involved. The concept and detailed procedure of this methodology is first introduced in this study. After that, the proposed methodology is applied to a case study bridge, i.e., unbonded laminated rubber bearing (ULRB) supported highway bridge retrofitted with transverse steel damper (TSD), to investigate how the uncertainty of the concerned model parameters affects the seismic fragility of the selected benchmark bridge when separately using the residual and peak displacements as engineering demand parameters (EDPs). The parameter sensitivity of fragility corresponding to these two EDPs is then quantified by a new proposed index and ranked using Tornado diagrams. In addition, the seismic fragility result when considering different sources of uncertainty is compared and discussed in this study through the parameterized fragility model. The result shows whether considering the uncertainty of model parameters has a more considerable influence on the fragility result when using the residual displacement as EDP as compared with using peak displacement as EDP. The sensitivity ranking of the parameters is affected by the damage level or the nonlinearity of the structural system. Regarding the residual and peak bearing displacements of ULRB-supported highway bridges, the friction coefficient is the most sensitive parameter.

Failure analysis of multi-span masonry arch bridges using combination of point cloud data and three-dimensional numerical modeling

Historical structures, including historical bridges, are part of cultural heritage, conveying the traces and characteristic features of past civilizations. To protect historical structures, it is necessary to prepare their 3D photogrammetric documentation, determine detailed geometric and material properties and perform computer-aided structural analysis using appropriate modeling techniques. The aim of this study is to present an effective, reliable and fast multidisciplinary approach for the analysis of historical masonry bridges. The aforementioned approach was illustrated with an example of the historical Halilviran masonry arch bridge and its behavior under possible loadings. Terrestrial laser scanning (TLS) was used to determine the bridge geometry with high accuracy. Point cloud data obtained from TLS was simplified and a three-dimensional CAD-based solid model of the structure was created. The Halilviran Bridge case study summarized in this report was conducted to examine the technical feasibility of using la¬ser scanning technologies for obtaining as-built records for similar historic bridges. A secondary objective was to identify other applications of this technology, notably for other transportation structures, and use numerical methods to assess the seismic behavior and failure model of the bridge. The seismic behavior of the bridge was examined using a finite-element- based macromodeling technique. Nonlinear dynamic analyses were carried out subsequently to identify the most susceptible regions of the bridge. Interpretation of the results, presented in the form of contour plots illustrating tensile damage and maximum displacements, offered a comprehensive depiction of the seismic response across the entire bridge. The methodology employed in this investigation can be viewed as a robust framework for evaluating the seismic response and potential failure of historical structures.

Machine Learning Clustering Techniques to Support Structural Monitoring of the Valgadena Bridge Viaduct (Italy)

The lack of precise and comprehensive information about the health of bridges, and in particular long span ones, can lead to incorrect decisions regarding maintenance, repair, modernization, and reinforcement of the structure itself. While the consequences of inadequate interventions are quite apparent, incorrect decisions can also result in unnecessary or misdirected actions. For example, an inadequate assessment of the structural health can lead to the modernization and replacement of some components that are still sound. Structural Health Monitoring (SHM) involves the use of time series derived from periodic measurements of the structure’s behavior, considered in its operational and load environment. The goal is to determine its response to various solicitations and, in particular, to highlight any critical issue in the structure’s behavior that may affect its reliability and safety due to anomalies and deterioration. This paper proposes an SHM method applied to the Valgadena bridge, one of the tallest viaducts in Italy and Europe (maximum height 160 m), located on the Altopiano dei Sette Comuni in the Province of Vicenza. Despite the fact that the viaduct itself had already been monitored during its construction using classical geometric leveling techniques, the methodology proposed here is based instead on the use of affordable dual-frequency GNSS (Global Navigation Satellite System) receivers to determine static and dynamic components of the bridge movements. Specifically, an effective combination of time series analysis methods and machine learning techniques is proposed in order to determine the vibration modes of the monitored viaduct. Monitoring is performed in regular operation conditions of the bridge (operational modal analysis (OMA)), and the use of certain machine learning methods aims at supporting the development of an effective automatic OMA procedure. To be more specific, the random decrements technique is used in order to make the vibration characteristics of the collected signals more apparent. Time-domain-based subspace identification is applied in order to determine a proper model of the collected measurements. Then, clustering methods, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and GMMs (Gaussian Mixture Models), are used in order to reliably estimate the system poles, and hence the corresponding vibration characteristics. The performance of the considered methods is compared on the Valgadena bridge case study, showing that the use of GMM clustering reduces, with respect to DBSCAN, the impact of the choice of certain parameter values in the considered case.

Geographical Factors Influencing Public Participation in Architectural Heritage Conservation: A Case Study of Chinese Wooden Arch Corridor Bridges

Public participation in architectural heritage protection is a crucial part of promoting the development of heritage conservation. However, even in the same country, different regions protect heritage at different levels. This study, taking national wooden arch corridor bridges as the research object, analyzes the public participation level in wooden arch corridor bridge protection in the last 5 years, studying the factors that influence public participation and the change in rules of public participation caused by geography, providing a theoretical reference for the sustainable development of the protection of wooden arch corridor bridges. The results indicate (1) public participation in the protection of wooden arch corridor bridges has been increasing annually under government guidance; (2) there is a positive correlation between the concentration of wooden arch corridor bridges and the level of public participation; (3) the better the planning is regarding wooden arch corridor bridges and their surrounding areas, the higher the level of public participation, while the lower the level of residents’ participation and willing; (4) the higher the development and better the transportation in the areas where wooden arch corridor bridges are located, the higher the level of public participation; and (5) increasing the popularity of wooden arch corridor bridges, as well as the areas they are located in, can enhance public participation. These conclusions and research methods also provide methodological guidance and theoretical support for other public participation in the study of architectural heritage.

State of the Art on Forensic Bridge Engineering in Chile. A Management and Technical Overview

Chile is a country highly affected by natural hazards, especially scour and seismic phenomena. Therefore, throughout the twentieth century and the beginning of the twenty-first, total or partial collapses of bridges have been evidenced, particularly those that occurred in the 1960 and 2010 earthquakes. From these experiences, the Chilean Highway Administration has promoted a series of management activities in order to reduce the number of collapsed structures. In this context, since 2019 the Ministry of Public Works of Chile (MOP) supported by the Chilean universities started a program to develop the subject of forensic engineering on bridges, in order to include the methodology in the technical codes and provide a standardization of protocols for the implementation of activities in the case of the collapse of structures. The aspects included in this methodology are: Determination of volatile and non-volatile information; Actions for intervention on the structure and protection of users; Gathering of technical information through the application of tests; Review of stress–strain states through Finite Element Method (FEM); Elaboration of hypotheses and review of them through a decision tree; and, finally, Conclusions and recommendations from a technical and contractual perspective. This paper presents a detailed description of the current forensic engineering methodology in Chile and its first application in the Cancura Bridge case study. In addition, a historical compilation of Chilean bridge collapses is presented, in each case identifying the typology of the structure, the causes of collapse and the main inspection techniques used. Finally, a statistical review is carried out analyzing aspects such as stages of the life cycle where collapse has occurred: design, construction or operation, and the incidence of natural hazards in these phenomena. From this analysis, the cases of Loncomilla, Cancura and Huasco bridges are highlighted. Since the collapse of these bridges occurred without the implementation of current methodology, the article concludes on the advantages and disadvantages of the methodology, providing a comparative analysis with the inspection activities in these structures.

Constraints of local stiffness adaption for reduced-order models of large-scale steel bridges

Large and complex structures, such as bridges, are subjected to a variety of environmental factors and loads over time, leading to evolving structural states. To assess their current condition and plan predictive maintenance, Digital Twins enriched with Structural Health Monitoring (SHM) data are essential. However, for large-scale structures, the creation of physics-based Digital Twins necessitates reduced-order modeling to ensure efficient adaptation to SHM data. Many of these methods for structural damage assessment rely on localized stiffness reduction to characterize discrete damage states. The Static Condensation Reduced Basis Element (SCRBE) method is a promising approach for model order reduction by decomposing the structure into parametric components. However, SCRBE's effectiveness and the accuracy of the Digital Twin depend on an appropriate partitioning of the structure into to components with reduced stiffness. This paper systematically investigates the process and the constraints of component selection for steel bridges with discrete damages, considering factors like numerics, sensor placement and complex geometries. A comprehensive analysis is conducted using a real bridge case study augmented with synthetic measurement data representing damage scenarios. The results reveal both challenges and the potential of the SCRBE method for Digital Twins. A systematic approach for component selection within the SCRBE framework is proposed, specifically tailored for real-world application scenarios. This structured methodology facilitates the efficient and adaptive development of Digital Twins for large, intricate structures, such as steel bridges.

A Case Study of Integrating Terrestrial Laser Scanning (TLS) and Building Information Modeling (BIM) in Heritage Bridge Documentation: The Edmund Pettus Bridge

The Edmund Pettus Bridge, Selma, Alabama, a symbol of the American Civil Rights Movement and an exemplar of early 20th-century engineering, stands as a testament to the progress and challenges of its era. The bridge, recognized for its pivotal role in the 1965 “Bloody Sunday” conflict and the following Selma to Montgomery marches for voting rights, also represents significant engineering achievements with its distinctive design and construction methodology. In this study, the research team presents a comprehensive framework for documenting heritage bridges by utilizing Terrestrial Laser Scanning (TLS) technology, supplemented by other Reality Capture (RC) techniques, including Structure from Motion (SfM), 360-degree photography, and Unmanned Aerial Vehicle (UAV), and integrating the data within a Building Information Modeling (BIM) environment. The focus on the Edmund Pettus Bridge case study demonstrates how this novel approach can capture the intricate details of its structural and architectural features while preserving its historical narratives. The documentation outcomes, including a detailed BIM model and a set of Historic American Engineering Record (HAER) drawings, highlight the effectiveness of combining TLS and BIM in conserving unconventional heritage structures like bridges. This paper also discusses the technological challenges encountered, such as dealing with heavy traffic and environmental constraints during data acquisition and developing the BIM model and drawings. It outlines the strategies implemented to address these issues. This research contributes to preserving a severely under-represented American National Historic Landmark (NHL). It sets a precedent for documenting other non-building heritage structures, balancing technological advancements with historical integrity.

Experimental response characterization and comparative seismic performance assessment of a long-stroke shape memory alloy bridge restrainer

In this study, a long-stroke SMA Restrainer (LSR) device was fabricated and its response was characterized through experimental testing. The LSR consists of a long SMA bar encased in a steel tube to prevent its buckling. First, an optimal heat treatment procedure was identified to obtain superelastic response for SMA bars. Subsequently, the LSR underwent testing using an increasing amplitude tension-compression loading protocol and its mechanical properties such as equivalent stiffness and equivalent viscous damping ratio were computed. Based on the experimental findings, a high-fidelity 3D finite element model of LSRs was developed, which then used to design a full-scale LSR and verify its performance. Nonlinear time history analyses were conducted to assess the seismic performance of the proposed restrainers when used in a four-span simply supported bridge structure. The response of a case-study bridge equipped with steel cable restrainers, SMA bar restrainers, or LSRs were comparatively evaluated using a set of 40 near-fault ground motion records. Both SMA bar restrainers and LSRs were able to effectively limit the displacement response and provide the re-centering capabilities, while LSRs producing similar moments on piers compared to steel cable restrainers. In addition, compared to tension-only SMA bar restrainers, the LSRs require about only a quarter of SMA materials used in SMA bar restrainers.

Fusing infrastructure health monitoring data in point cloud

This paper presents a unique approach to data fusion in the condition assessment of civil infrastructure systems. The approach fuses dynamic monitoring sensor data with the visual data collected from a bridge structure. Such a data fusion method, named ModeShapeFuser, enables the visualization of mode shapes of engineering structures in their point cloud, offering a higher spatial resolution model of the dynamic behaviors of existing structures. A bridge case study shows that utilizing this immersive data visualization technique can potentially provide a more intuitive and holistic understanding of the dynamic behavior characteristics of engineering structures. The outputs of ModeShapeFuser, the high spatial resolution models of the bridge, are also presented in a Virtual Reality (VR) environment through a head-mounted display and a VRTable, providing a virtual tour of the bridge in its real environment. The study presented herein is critical for an informed decision-making process in assessing infrastructure systems.

Evaluation of geosynthetic encased columns in Zambian heterogenous soils

The development of Zambia has brought increased large-scale infrastructure construction that has necessitated the need for improved foundation techniques that are both economical and adequate in capacity. Clay and soft soils with low bearing capacities and high compressibility could render structural foundations to perform poorly and shorten the design life of the bridges and structures. This study used a bridge case study in Northern Province, Zambia to investigate the use of geosynthetic encased columns (GECs) to support the bridge embankments to reduce differential settlements. End bearing fully encased columns were compared to floating columns of varying lengths by numerical modelling in PLAXIS 3D. The Hardening Soil and Mohr–Coulomb soil models were used for the column surrounding soil and the GECs in the finite element analysis. The results showed that the end bearing columns had the least differential settlements at the soil surface, whilst the reduction in floating column length increased the punching settlements. Moreover, the shear stress along the interface of the GECs and surrounding soil varied from 20 kN/m2 to 142 kN/m2, where the end bearing GEC had the least shear stress.

Determination of cable tension force in pedestrian suspension bridge short hangers based on finite element model updating

Abstract The hangers are the important element on suspension bridges that transfer the forces from the deck to the main cable. To verify their capacity and identify possible risks, the forces transferred by the hangers and the corresponding tension must be determined. However, this is often done using standard equations for determining cable forces. Due to the different lengths of the hangers and the way in which their tensioning is achieved, the effects of boundary conditions occur that need to be considered and that require an update of the finite element model at the local level (when the whole bridge structure is observed). This paper presents a method for determining the cable tension of hangers that combines the experimentally determined dynamic properties (natural frequencies and mode shapes) and the numerical model updating. In addition to the usual approach based on the determination of the natural frequency of hangers, this paper presents an approach based on the mode shapes of the hangers. Special attention is paid to the boundary conditions coefficient. The method is applied on suspension bridge case study.

A generalized, model-free framework for assessing scour-induced bridge pier instability

Flood-induced scour is one of the leading causes of bridge failures in the United States, accounting for 53 % of recorded failures between 1989 and 2000. With many existing bridges considered “scour critical”, it is essential to closely monitor its development for effective mitigation. However, despite advances in scour hole measurement techniques, continuous monitoring of scour remains underused and little guidance is available to maintenance engineers to assess structural stability from scour depth measurements. This research presents a proof-of-concept, model-free bridge pier structural assessment framework. This framework is built on a library of bridge pier failure surfaces – curves that define failure limits for combinations of scour, wind, and flood loading for a range of structural configurations and site conditions. User input is limited to basic structural and site information, from which a corresponding failure surface is automatically selected, either directly or through interpolation between surfaces in the library. In this study, the creation of a failure surface library and framework is focused on pile bent pier designs, due to their widespread use. A broad scope of parameters was modeled such that common configurations of pier bent bridge designs and site conditions in Florida were considered. Sensitivity studies were performed to identify parameters for which modeling simplifications could be made to build the failure surface library with reduced computational burden. Results from three case study bridges are presented to verify that the resulting assessment framework maintained reasonable representation of structural response.

An active learning method using deep adversarial autoencoder-based sufficient dimension reduction neural network for high-dimensional reliability analysis

Reliability analysis often requires time-consuming evaluations, especially when dealing with high-dimensional and nonlinear problems. To address this challenge, surrogate model methods are frequently employed. One way to improve the efficiency of surrogate model methods involves selecting informative samples that significantly enhance the accuracy of the surrogate model. This paper introduces a novel approach to facilitate the construction of surrogate models and selection of informative samples in high-dimensional reliability analysis, through an active learning method based on a deep adversarial autoencoder-based sufficient dimension reduction (AAE-SDR) neural network. The AAE-SDR neural network serves as a surrogate model, transforming complex high-dimensional variables into tractable, low-dimensional embeddings relevant to the target. These embeddings are Gaussian-distributed with a distinct latent limit state boundary. A new sampling strategy is proposed to select informative misclassified samples by iteratively identifying candidate samples near the latent limit state boundary and uniformly sampling from the candidate sample dataset based on the latent Gaussian distribution. The effectiveness of the proposed approach is demonstrated through two high-dimensional numerical examples and a cable-stayed bridge case study. Results show that the proposed method simplifies complex high-dimensional reliability problems and provides a relatively accurate estimated failure probability with a limited number of samples.

A discussion about the limitations of the Eurocode’s high-speed load model for railway bridges

High-speed railway bridges are subjected to normative limitations concerning maximum permissible deck accelerations. For the design of these structures, the European norm EN 1991-2 introduces the high-speed load model (HSLM)—a set of point loads intended to include the effects of existing high-speed trains. Yet, the evolution of current trains and the recent development of new load models motivate a discussion regarding the limits of validity of the HSLM. For this study, a large number of randomly generated load models of articulated, conventional, and regular trains are tested and compared with the envelope of HSLM effects. For each type of train, two sets of 100,000 load models are considered: one abiding by the limits of the EN 1991-2 and another considering wider limits. This comparison is achieved using both a bridge-independent metric (train signatures) and dynamic analyses on a case study bridge (the Canelas bridge of the Portuguese Railway Network). For the latter, a methodology to decrease the computational cost of moving loads analysis is introduced. Results show that some theoretical load models constructed within the stipulated limits of the norm can lead to effects not covered by the HSLM. This is especially noted in conventional trains, where there is a relation with larger distances between centres of adjacent vehicle bogies.

Fragility curves derivation under fire actions for different types of bridges

The Italian infrastructure network of bridges is complex, due to the orography of the territory and the presence of different hazard sources. Furthermore, during their service life these infrastructures can be subjected also to extreme actions such as the fire. This paper presents a procedure for the fragility curves derivation under fire actions for different types of bridges. The novelty the work is to apply the fragility assessment also to the fire resistance aspects, especially related to the infrastructures. Two case study pilot R.C. and composite steel-R.C. bridges are modelled by assigning combined loads according to the fire load combination and deriving structural response under fire scenarios. By using the linear regression, the structural response parameters of the bridges are correlated with fire intensity, considering different performance levels. Finally, lognormal fragility curves are obtained, measuring the capacity of the case study bridges. The study also shows which of the fire scenario parameters can be better correlated to the structural response of the bridges.

A Bayesian network-based framework for SHM data fusion supporting bridge management

Guidelines for Risk Classification and Management, Safety Assessment and Monitoring of Bridges, issued in 2020 by the Italian Ministry of Sustainable Mobility, promote the use of Structural Health Monitoring (SHM) systems for risk assessment of bridges. Prestressed or ordinary reinforced concrete bridges are designed to maintain their functionality over a long period of time. However, during their service life, such structures may be exposed to ever- increasing traffic volumes, extreme weather conditions and/or marine environments and so on. Therefore, constant maintenance and timely interventions are crucial to ensure the functionality of the transportation network. The current management process is mainly based on visual inspections, while the aggregation of information coming from multiple sources is still a major challenge. In this context, the main objective of the present work is to develop a general Bayesian framework capable of processing the different sources of information based on a Bayesian network (BN), a probabilistic graphical model that represents a set of variables and their conditional dependencies. A BN is a useful tool for the management of bridges since it allows the prediction of damage states and failure conditions based on the knowledge by visual inspections and SHM. The combined use of SHM and Bayesian approaches can reduce the overall risk of failure increasing the efficiency of the infrastructure system. The procedure is exemplified and validated with reference to a simply supported post-tensioned case study bridge.