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As critical infrastructures, bridges play an important role in the resilience and functionality of the transportation system. Typically, in the aftermath of an earthquake, the restoration process of the impacted region begins. It is vital for bridges to maintain their functionality and serviceability during this period in order to expedite the restoration process. In this regard, restoration functions are used to assess the functionality of bridges in a quantitative manner before an extreme event. This paper presents probabilistic resilience curve for the Chemin des Dalles Bridge (CDB) in Quebec, Canada by incorporating fragility and restoration profiles available in the literature. The CDB is designed according to older design codes, which they may lack seismic detailing, and it could therefore be susceptible to future earthquake damage. The resilience curves obtained were used to quantify the resilience of the CDB. The results indicate that the bridge has a resilient performance in a code-level earthquake. However, in order to improve resiliency in stronger events, the retrofit of vulnerable components should be considered. [ABSTRACT FROM AUTHOR]

• Data-driven model to classify damage state of RC beams under blast loading with superior accuracy was developed. • Machine learning-integrated reliability framework was introduced to quantify probability of occurrence of blast damage states considering uncertainty. • New framework gives insight into post-blast damage assessment of RC beams while eliminating extensive modeling & computational demands. • Blast Reliability Curves illustrate probabilities of RC beam damage states and their transition across wide spectrum of blast intensities. • Damage state probability most sensitive to blast intensity & tension reinforcement ratio, concrete & steel strength became prominent at higher reinforcement ratios. The incessant demand for protective infrastructure research has been motivated by recent accidental and intentional explosion events. This has led to a significant increase in blast design approaches and modern methods of structural analysis. When considering the analysis and response of blast-loaded members, like the identification of crack patterns and failure modes (CP & FM) of reinforced concrete (RC) beams, the current literature heavily depends on complex numerical modeling. Recently, the adoption of machine learning (ML) modeling to readily predict damage state labels of blast-loaded RC beams has emerged as a competitor to numerical methods. In either modeling method, outputs of damage states are often oversimplified as deterministic labels without elaborating on the possibility of alternative damage state. To extend on the practical usage of deterministic modeling, the present study introduces a probabilistic framework which integrates ML models towards ascertaining the probability of different CP & FM classes via the Monte Carlo simulation. The framework accounts for the uncertainties of different beam and blast parameters and incorporates four damage states of flexural cracking, bending failure, flexural-shear cracking, and crushing failure. The developed framework was evaluated against forty-two blast-loaded RC beams available from several previous experimental studies and exhibited excellent predictive performance while providing significant insight to the occurrence of all four damage states. In addition, extensive Blast Reliability Curves (BRC) were developed for eighteen RC beam cases over a wide range of blast intensities and the effects of beam and blast parameters on qualitative damage states were evaluated. The most notable observation was that the sensitivity of both concrete and steel strengths on damage state probabilities increased with reinforcement ratio. The BRCs depicted an elaborate transition of damage states with varying blast magnitudes. Also, the limitations, sources of error, and further improvements associated with the proposed methodology were clearly defined. Overall, the resulting probabilistic structural blast analysis introduced by the proposed framework serves as an intricate approach to providing greater comprehension to the CP & FM response of RC beams under blast loading which aims to promote confidence in decision making within protective design processes. [ABSTRACT FROM AUTHOR]

• Investigation of the effect of wave model selection on the probability of structural damage. • Examination of the sensitivity of wave models to the selected intensity measures. • Provision of preliminary insights on the proper wave model selection based on fragility analysis. • Development of parameterized fragility models for the regional risk assessment of port structures. Pile-supported wharves and piers may suffer structural damage during extreme coastal events, which may result in port closures that can cause significant economic losses. Risk analysis can estimate the post-hazard functionality of ports though the structural damage assessment of these structures prior to coastal events. However, assumptions on the selected demand estimates may significantly affect the estimated probability of structural damage. Therefore, this study aims to shed light on the sensitivity of the wave model selection for the risk assessment of pile-supported wharves and piers when subjected to storm surge and waves. The examined structural damage is the uplift of the deck, and the risk assessment is conducted through the development of fragility curves for a typical deck-pile connection, for which fragility curves are developed for different wave models. Uncertainties are also considered in parameters affecting the demand and capacity of the examined deck-pile connection and are propagated through the Monte Carlo simulation using the Latin Hypercube Sampling. The results indicate changes to the uplift probability because of the selected wave model. Thus, wave model selection can significantly affect the uplift failure probability. In addition, the study proposes parameterized fragility models to enable the uplift risk assessment across a region. The presented results aim to throw light on the proper model selection to produce more realistic risk assessment estimates towards the resilience of coastal infrastructure. [ABSTRACT FROM AUTHOR]

This study proposes a framework for the seismic fragility assessment of steel moment-resisting frames exposed to different rates of corrosion and subjected to real mainshock-aftershock (MS-AS) sequences. The inherent uncertainties in the structural characteristics may significantly affect the structural performance. Thus, in addition to the demand parameters, the framework considers randomness in the capacity parameters using the Latin hypercube sampling. Three frames are modeled in OpenSees and then subjected to nine real as-recorded MS-AS sequences to illustrate the application of the framework. Four limit states are defined based on the maximum interstory drift ratio and the multiple stripe analysis is chosen for the fragility assessment. The presented framework allows for the plot of the fragility surfaces for each corrosion level conditioned on both the mainshock and the aftershock intensity. The results indicate that corrosion and aftershock may significantly affect the seismic fragility especially for the extreme limit states. • Proposes a novel fragility framework for steel frames subjected to corrosion and real mainshock-aftershock sequences. • Evaluates the corrosion vulnerability based on the lifetime of the building rather than corrosion percentages. • Supports not only demand-driven random variables but also capacity-driven random variables. • Develops MS-AS fragility surfaces to reflect the characteristics of MS-AS sequences. • Compares the fragility surfaces for low-, medium-, and high-rise steel moment-resisting frame buildings. [ABSTRACT FROM AUTHOR]

Installation of building utilities often requires the creation of openings near existing slab–column connections. These openings reduce the concrete area that sustains the shear stresses, thus, the developed shear stresses are increased and contribute to the risk of punching shear failure. In this paper, nonlinear finite element analysis (FEA) using a previously calibrated coupled plasticity‐damage model for concrete is performed for interior slabs with square openings. An interior reinforced concrete flat slab without shear reinforcement, which was tested under gravity loading, is considered as the reference slab. The effect of the location and the size of the opening on the punching shear resistance are examined. Punching shear predictions according to ACI 318‐14, Eurocode 2‐2004, and fib Model Code 2010 are compared to the FEA results. Finally, probabilistic analyses using the Monte Carlo simulation are considered, and fragility analysis is performed to estimate the probability of the punching shear resistance related to the size and distance of the opening. [ABSTRACT FROM AUTHOR]

The rise of Performance Based Design methodologies for fire safety engineering has increased the interest of the fire safety community in the concepts of risk and reliability. Practical applications have however been severely hampered by the lack of an efficient unbiased calculation methodology. This is because on the one hand, the distribution types of model output variables in fire safety engineering are not known and traditional distribution types as for example the normal and lognormal distribution may result in unsafe approximations. Therefore unbiased methods must be applied which make no (implicit) assumptions on the PDF type. Traditionally these unbiased methods are based on Monte Carlo simulations. On the other hand, Monte Carlo simulations require a large number of model evaluations and are therefore too computationally expensive when large and nonlinear calculation models are applied, as is common in fire safety engineering. The methodology presented in this paper avoids this deadlock by making an unbiased estimate of the PDF based on only a very limited number of model evaluations. The methodology is known as the Maximum Entropy Multiplicative Dimensional Reduction Method (ME-MDRM) and results in a mathematical formula for the probability density function (PDF) describing the uncertain output variable. The method can be applied with existing models and calculation tools and allows for a parallelization of model evaluations. The example applications given in the paper stem from the field of structural fire safety and illustrate the excellent performance of the method for probabilistic structural fire safety engineering. The ME-MDRM can however be considered applicable to other types of engineering models as well. [ABSTRACT FROM AUTHOR]

The main function of the concrete containment structures is to prevent radioactive leakage to the environment in case of a loss of coolant accident (LOCA). The Canadian Standard CSA N287.6 (2011) proposes periodic inspections, i.e., pressure testing, in order to assess the strength and design criteria of the containment (proof test) and the leak tightness of the containment boundary (leakage rate test). During these tests, the concrete strains are measured and are expected to have a distribution due to several uncertainties. Therefore, this study aims to propose a probabilistic finite element analysis framework. Then, investigates the relationship between the concrete strains and the prestressing loss, in order to examine the possibility of estimating the average prestressing loss during pressure testing inspections. The results indicate that the concrete strain measurements during the leakage rate test may provide information with respect to the prestressing loss of the bonded system. In addition, the demonstrated framework can be further used for the probabilistic finite element analysis of real scale containments. [ABSTRACT FROM AUTHOR]

Finite element reliability analysis (FERA) has been used to evaluate the reliability of structures. In FERA, approximate methods are commonly used to estimate the mean and variance of the structural response, while its probability distribution is primarily derived based on the Monte Carlo simulation (MCS) method. This paper advances FERA by combining it with the multiplicative dimensional reduction method (M-DRM). The proposed M-DRM allows fairly accurate estimation of the statistical moments, as well as the probability distribution of the structural response. The distribution of the response is obtained using fractional moments, which are calculated from the M-DRM, along with the maximum entropy principle. The variance of the response, based on global sensitivity measures, is obtained as a by-product of the analysis. The proposed approach is integrated with the OpenSees software and is illustrated through examples of nonlinear finite element analyses of reinforced concrete and steel frames. The paper shows that the proposed approach is an accurate and efficient alternative for FERA. [ABSTRACT FROM AUTHOR]

In this paper, probabilistic finite element analysis (FEA) is applied using the Monte Carlo simulation (MCS) and the multiplicative dimensional reduction method (M-DRM). M-DRM is proposed for stochastic FEA of large scale and/or complex problems, as it provides the probability distribution of the structural response, apart the statistical moments, and requires fairly small computational time. MCS and M-DRM results are compared, indicating that both are in a good agreement. In addition, sensitivity analysis is also performed using the M-DRM, which does not require any extra analytical effort. The probabilistic FEA is applied with the use of the ABAQUS software, where the development of the FEA model and the updating of each input random variable for the required simulations, are both implemented with the use of the Python programing language. Two previously tested reinforced concrete flat slabs, with and without shear reinforcement, are examined. The concrete damaged plasticity model is used for the modeling of the concrete, which is offered in ABAQUS. The results of the deterministic FEA simulation show reasonable response compared to the behavior of the test specimens in terms of ultimate load, deflection and cracking propagation. For the probabilistic analysis, only the material uncertainty is taken into account, in order to examine the accuracy and efficiency of the proposed M-DRM framework and the contribution of the material uncertainty to the output response. Finally, design codes (ACI 318-11 and EC2 2004) for punching shear and the critical shear crack theory (CSCT 2008, 2009) are examined, considering the same input uncertainties. Useful outcomes are presented indicating the predictive capability of the proposed probabilistic FEA for future studies. [ABSTRACT FROM AUTHOR]

• Development of parameterized fragility models for port structures subjected to storm surge and waves. • Inclusion of time-dependent models for capturing structural aging and deterioration. • Comparison of surrogate models for classification problems in coastal fragility modeling. • Applicability of the developed models to region vulnerable to hurricanes. This study proposes a methodology for the development of parameterized fragility models for open type port structures subjected to hurricane-induced storm surge and wave loading, while also considering the potential influences of time-dependent aging and deterioration. The proposed framework uses a simulation-based strategy to propagate uncertainty in loads, geometrical configurations, material properties and capacities associated with different exposure conditions and failure modes (i.e. uplift, flexure, shear). Alternative classification-type statistical learning methods are tested to derive parameterized fragility models for each failure mode in order to evaluate limit state exceedance probability given a vector of deterministic or probabilistic parameters that define the structure and exposure conditions. Such parameterized coastal fragility models enable rapid application to a portfolio of aging port structures to explore the impact of current and future storm conditions, as illustrated via case study application for two terminals located in the Houston Ship Channel. The results reveal the dominant failure mode, spatial distribution of failure probabilities for structures within and across terminals, and influence of aging on surge and wave vulnerability of port structures. [ABSTRACT FROM AUTHOR]

During the vulcanization process of steel-laminated elastomeric bridge bearings, unavoidable flow of elastomer occurs inside the mold under high pressure and nonuniform heating. This may cause the interior steel laminates to misalign or distort in the finished bearing. There has been limited research to understand and quantify the effects of manufacturing imperfections, such as vertically shifted, rotated, and bent steel laminates, on the mechanical behavior of elastomeric bearings. This paper presents a finite element investigation on the effects of manufacturing imperfections on the performance of elastomeric bridge bearings subjected to service-level loads. It is observed that such imperfections have a negligible effect on the maximum shear strain induced in the elastomer. The strain hardening behavior of the elastomer under relatively large shear strains limits the maximum shear strains to values lower than those predicted by the pressure solution. In one cycle of loading, the vertical deflection of bearings is affected slightly by these imperfections. However, manufacturing imperfections can increase the tensile stresses in the steel laminates causing plastic damage. The spread of plastic damage in the steel laminates with repeated cycling may affect the long-term performance of the bearings. [ABSTRACT FROM AUTHOR]

Elastomeric bridge bearings are installed between the bridge superstructure and substructure to accommodate translational and rotational deformations. The manufacturing quality of elastomeric bridge bearings is of high significance because manufacturing defects (such as variations in rubber layer thickness and nonparallel steel laminates) may jeopardize their short- and long-term structural behavior and integrity. Current quality control procedures involve destructive testing of samples of bearings from a lot. This type of testing is time consuming and costly, and thus limited to a relatively small sample size, which may undermine confidence in the quality of remaining bearings in the lot. This paper presents an alternative, vision-based assessment methodology for the nondestructive identification of the internal structure of elastomeric bridge bearings. The methodology capitalizes on the high deformability of rubber and the near inextensibility of the steel laminates, which together result in a unique deformation pattern on the vertical surfaces of a bearing when it is subjected to axial load. This deformation pattern features local extrema in in-plane strain and horizontal displacement fields on the vertical surfaces. These local extrema are analyzed to deduce the thicknesses of the rubber layers and rubber side covers. The methodology is developed based on three-dimensional finite-element analyses (3D-FEA). Then, three-dimensional digital image correlation (3D-DIC) is used in experimental tests to evaluate its capability to identify manufacturing defects in elastomeric bridge bearings. Finally, the methodology is validated against destructive tests. The nondestructive method presented in this study is conducted in conjunction with compressive tests that departments of transportation carry out routinely and is therefore expected to facilitate rapid and cost-effective qualification of elastomeric bridge bearings. [ABSTRACT FROM AUTHOR]

• Parameterized fragility models for individual spans of different bridge classes. • Inclusion of spatial variability of wave loads and variations in structural characteristics. • Bridge system fragility assessment framework for system reliability assessment. • Investigation of bridge performance across a region for different storm scenarios. Past hurricanes in the United States have highlighted the structural vulnerability of bridges exposed to storm surge and wave loads, triggering the development of fragility models. These models are essential for estimating the likelihood of structural failures in bridges during extreme events to support risk mitigation and resilience enhancement. However, the literature still lacks fragility models that can accommodate the effects of spatial variability of surge and wave loads and bridge characteristics like superstructure type and span slope. In addition, the presence of different types of superstructure within the same bridge necessitates the development of fragility models which can handle such variations. In this light, first, this study develops fragility models for individual spans of different bridge classes (concrete girder, steel girder, slab, box girder) considering spatial variability of wave loads and variations in structural characteristics. Herein, nonlinear dynamic analyses are employed for calculating the structural response and stepwise logistic regression is used for deriving the predictive fragility models. Then, a bridge system fragility assessment framework is proposed for system reliability assessment of the entire bridge, which accounts for different types of superstructures within the bridge and partially correlated span failures. In order to demonstrate the application of the fragility models and the system fragility assessment framework for regional level risk mitigation, the performance of bridges in the Houston-Galveston region is evaluated for two storm scenarios. The results show that the system failure probability obtained using no correlated span failure assumption is close to the actual failure probability obtained using the proposed approach highlighting the effects of correlations (or the lack of) on the system fragility. [ABSTRACT FROM AUTHOR]

This study aims to advance knowledge on multi-hazard damage to shallow foundation bridges subjected to simultaneous scour, earthquake, and vehicular loads. While most water-crossing bridges have deep foundations, still, a significant number of water-crossing bridges have shallow foundations. In addition, there is limited research focusing on damage assessment of bridges with shallow foundations under multi-hazard scenarios. Therefore, this study proposes a probabilistic multi-hazard damage assessment framework for bridges with shallow foundations exposed to seismic, scour, and vehicular loadings. The proposed framework includes steps for hazard modeling, multi-hazard finite element modeling, non-linear time history analysis, and multi-hazard fragility modeling, and it was showcased on Chemin des Dalles Bridge, which is a typical bridge located in the Province of Quebec in Canada. Demand and fragility models parameterized on hazard and soil parameters were developed for the bridge components and a system fragility model was also developed using a series system assumption. The results from the analysis of these damage and fragility models indicated that scour notably increases component- and bridge-level response in typical shallow foundation bridges. However, until the scour depth is lower than the foundation height, the increased damage does not impose a significant threat to the bridge's safety. It was observed that earthquakes had the highest influence on the bridge components' response followed by scour and vehicular loads. Overall, the proposed framework can be used to facilitate policy-making toward informed bridge maintenance and retrofitting to aid in disaster mitigation planning and emergency response. • Novel framework for multi-hazard damage assessment of shallow foundation bridges. • Uniform scour around foundation had little impact on shallow foundation bridges. • Influence of hazards, high to low, was Earthquake, Scour, Vehicular load. • Soil quality's effect was greater than scour depth on shallow foundation bridge's response. • 3D Finite-element modeling of scoured shallow foundation bridges was presented. [ABSTRACT FROM AUTHOR]

Seismic evaluations of bridges are critical to ensure the post-earthquake integrity of transportation networks; however, systematic evaluations are costly. Hence, bridge management systems have developed screening policies to identify the most seismically vulnerable bridges. Nevertheless, such policies are typically qualitative and may not easily capture the impact of bridge aging or the uncertainty in material and structural parameters, leading to potentially underrated vulnerability estimates. Thus, a new quantitative-yet-practical framework is proposed to screen bridges given the fragilities of deteriorating critical components, namely bearings and columns. The framework uses fuzzy-logic to integrate deterioration data from routine visual inspections into fragility analysis. Then, parameterized fuzzy-fragility models are developed for columns and bearings given their deterioration level, bridge design parameters, and seismic intensity. These models are utilized to develop a seismic vulnerability ranking system to set bridge priorities for detailed seismic investigation. The analysis revealed that bridge vulnerability ranking could significantly increase with deterioration. • Proposes a practical framework to estimate seismic fragility of deteriorating bridges. • Formulates fuzzy condition indices based on visual inspection to update fragility estimates. • Develops fuzzy-fragility models function of condition and design parameters. • Demonstrates how fuzzy-fragility models can be used to rank the seismic vulnerability of bridges. [ABSTRACT FROM AUTHOR]

• Framework to evaluate the accessibility of NaTech accidents during storm surge events. • Storage tank fragility models to identify locations and likelihood of NaTech events. • Bridge fragility models and GIS analysis to determine road network disruptions. • Probabilistic network analysis to quantify time-evolving accessibility metrics. • Benefits of framework are demonstrated for a case study region in Texas. Recent hurricane events have exposed the susceptibility of petrochemical facilities to severe transportation network disruptions due to flooding or storm surge. Network disruptions can result in cascading impacts or amplify the consequences of damage to petrochemical infrastructure due to delayed emergency response and limited access to the site. This study presents a scenario-based framework to assess the accessibility of petrochemical facilities by emergency responders and workers during storm surge events. First, the framework couples storm surge modeling with aboveground storage tank fragility models to determine the locations where natural hazard-triggered technological (NaTech) events could occur. Then, storm surge modeling is coupled with bridge fragility models and geographic system analysis to evaluate the potential for network disruptions such as bridge failures and road inundations. Finally, probabilistic network analyses are performed to evaluate the time-evolving accessibility of NaTech sites to emergency responders and facility workers. As a proof of concept, the framework is applied to a case study area. Results for the case study area demonstrate that the proposed framework is a powerful tool to quantify the accessibility of potential NaTech events, facilitate mitigation and emergency activities, and improve the management of critical resources and personnel during and after a storm. [ABSTRACT FROM AUTHOR]

• Identifies and quantifies manufacturing imperfections in elastomeric bridge bearings. • Compares the cyclic lateral response characteristics of different size non-seismic bearings. • Compares the lateral behavior of non-seismic bearings with and without imperfections. • Identifies the plastic damage extent and evolution in non-seismic bearings under seismic-level lateral demands. • Presents relevant recommendations for the design of elastomeric bridge bearings. Unbonded laminated elastomeric bridge bearings are designed to accommodate service-level deformations, but during an earthquake they can experience deformation demands that are significantly larger than those for which they were designed. Because these bearings are critical elements of a bridge, they must perform reliably even under seismic-level demands. The effects of the characteristics and condition of the steel shims on the seismic behavior of these bearings have received limited attention in the literature. Yet, the manufacturing process of these bearings sometimes introduces imperfections. For instance, during vulcanization, the steel shims might get rotated or bent. In this context, this paper investigates how manufacturing imperfections and the thickness characteristics of steel shims affect the behavior of unbonded elastomeric bridge bearings subjected to successive cycles of large lateral displacement. Finite element analysis is used to study the deformation pattern of these bearings, their hysteretic response characteristics, and the damageability of their steel shims. It is observed that imperfections can increase a bearing's roll-off and risk of instability. Imperfections can also increase a bearing's effective lateral stiffness and damage, quantified though a specific plastic-energy-based damage factor. Plastic damage in the shims is found to be appreciably dependent on their thickness. Under cyclic displacement, damage manifests itself more in the form of spreading of the yielded area rather than increased peak strain ductility. Plastic strains in the steel shims do not get close to the fracture limit, and all the bearings exhibit fairly stable hysteretic loops under the adopted loading protocol. [ABSTRACT FROM AUTHOR]

• Identifies bridge bearing lifetime demands for a range of typical highway bridge designs. • Develops prediction models for number of cycles and cycles displacements. • Explores trends in demands for various loading and bridge design parameters. • Demonstrates how prediction models can be used to derive customized bridge bearing loading protocols. A good replacement and maintenance policy for bridge bearings is essential for bridge integrity and functionality. This requires proper estimation of the bearing life expectancy which in turn is dependent on the working condition demands. The lifetime travel and peak displacement demands are highly sensitive to the loading, bridge geometry, and bearing properties. Hence, predicting when bearings should be replaced is difficult. To facilitate the decision making process, this study proposes prediction models for the annual demands of elastomeric bridge bearings. First, random bridge configurations (e.g. elements geometry, deck type, and number of spans) and random loading conditions (e.g. temperature profiles, earthquake records, and traffic loading scenarios) are generated using Monte Carlo simulation combined with Latin hypercube sampling, and the bearing demands are computed. Bearing demand prediction models are then developed via regression analysis and used to create a general fatigue loading protocol. This protocol can be used for testing and rating sample bearings. This would aid in predicting the bearing life expectancy, allowing for better replacement scheduling, and budget estimation. The application of the proposed demand prediction models for generating fatigue loading protocols is demonstrated through a case study. [ABSTRACT FROM AUTHOR]