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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]

• 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 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]

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]