The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The widely adopted assumption of uncorrelated Gaussian error may lead to inaccurate parameter estimation and overconfident predictions, which may lead to suboptimal decisions. This article addresses the challenges of performing Bayesian system identification for structures when large datasets are used, considering both spatial and temporal dependencies in the model uncertainty. We present an approach to efficiently evaluate the log-likelihood function, and we utilize nested sampling to compute the evidence for Bayesian model selection. The approach is first demonstrated on a synthetic case and then applied to a (measured) real-world steel bridge. The results show that the assumption of dependence in the model prediction uncertainties is decisively supported by the data. The proposed developments enable the use of large datasets and accounting for the dependency when performing Bayesian system identification, even when a relatively large number of uncertain parameters is inferred.

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The failure assessment line (FAL) describes the interaction between plastic failure and fracture of flawed steel components subjected to tension or bending. This paper quantifies the model uncertainty of the FAL as provided in the internationally used British Standard BS 7910:2019 by comparing the assessment with the actual failure load of 82 wide plate and 4 tubular joint tests. In line with findings of others, it is demonstrated that the accuracy of the assessment is significantly improved if the crack tip constraint is considered in the assessment. Irrespective of this crack tip constraint consideration, a non-negligible number of wide plate tests has a lower failure load than the one predicted by the FAL in BS 7910:2019, if based on three fracture toughness tests. A penalty or safety margin on the FAL is advocated to compensate for this. It appears advantageous to base the assessment on the average instead of the minimum of three equivalent fracture mechanics tests together with an associated (quantified) safety margin.

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This paper presents a reliability-based calibration method for design code formulas. The method is demonstrated on the shear design formulas in Eurocode 2 and fib Model Code 2010 (MC2010). We found that the partial factor (Formula presented.) in the current Eurocode 2 is about 20% lower than the optimal value and, thus, provides an insufficient safety margin. The obtained optimal partial factor (Formula presented.) in the (modified) Eurocode 2 and MC2010 formulas is 1.53 and 1.36, respectively. The difference stems from higher accuracy and, hence, lower uncertainty of the MC2010 model in predicting experimental results. Hence, on average, the MC2010 formula leads to about 13% larger design resistances compared to Eurocode 2 given that the target reliability for both design formulas is the same. To stimulate and facilitate future structural code development and derivation of partial factors, we make the used computer code freely available.

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This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image pairs are generated using a statistics-based approach and labelled by 28 structural engineering experts. When the data is randomly split into fit and test data, the SCNNs can achieve good performance on the test data ((Formula presented.)). When the SCNNs are tested on ”unseen” archetypes, their test (Formula presented.) values are on average 1% lower than the case where all archetypes are ”seen” during the training. These very good results indicate that SCNNs can generalise to unseen cases without compromising their performance. Although the analyses are restricted to the considered synthetic images, the results are promising and the approach is general.

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The degree of similarity between damage patterns often correlates with the likelihood of having similar damage causes. Therefore, deciding whether crack patterns are similar is one of the key steps in assessing the conditions of masonry structures. To our knowledge, no literature has been published regarding masonry crack pattern similarity measures that would correlate well with assessment by structural engineers. Hence, currently, similarity assessments are solely performed by experts and require considerable time and effort. Moreover, it is expensive, limited by the availability of experts, and yields only qualitative answers. In this work, we propose an automated approach that has the potential to overcome the above shortcomings and perform comparably with experts. At its core is a deep neural network embedding that can be used to calculate a numerical distance between crack patterns on comparable façades. The embedding is obtained from fitting a deep neural network to perform a classification task; i.e., to predict the crack pattern archetype label from a crack pattern image. The network is fitted to synthetic crack patterns simulated using a statistics-based approach proposed in this work. The simulation process can account for important crack pattern characteristics such as crack location, orientation, and length. The embedding transforms a crack pattern (raster image) into a 64-dimensional real-valued vector space where the closeness between two vectors is calculated as the cosine of their angle. The proposed approach is tested on 2D façades with and without openings, and with synthetic crack patterns that consist of a single crack and multiple cracks.@en

The modelling of bond behavior can be an important aspect in the reliability assessment of corroded reinforced concrete structures. However, existing models for bond properties of corroded reinforcement - to our knowledge - are not expressed in probabilistic terms and hence not considered in reliability analyses. This paper aims to fill this gap by (i) quantifying the uncertainties associated with a selected model that translates a reinforcing bar area loss to the change in bond-slip properties; and (ii) propagating these uncertainties to structural reliability to assess their importance. The frequentist paradigm is applied for the statistical analysis of experimental data for the bond model of corroded reinforcement proposed in the literature. Reliability assessment of an illustrative example - a simply supported beam with lap splices - is completed with and without considering the uncertainties in this bond model. The reliability calculations concern ultimate limit state verifications, using non-linear finite element analysis. The results show that the neglect of uncertainties in the bond model for corroded reinforcement can lead to about 10 times underestimation of the failure probability, hence their consideration in reliability assessment is advised.

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