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• 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]

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]

• 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]

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]

• 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]