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Post-earthquake observations have outlined the poor seismic performance of substandard reinforced concrete (RC) beam-column joints in existing buildings. They often exhibit significant cracking even under moderate intensity earthquakes, compromising the seismic performance of the entire building. This makes the quantification of their residual capacity and the definition of reliable repairability thresholds critical. Different approaches are available in the literature to establish the repairability of RC joints. However, a simple crack width-based criterion is required for practitioners during in-situ inspections. This study deals with the definition of crack width-based fragility curves relying on numerical analyses. Those are carried out by using the validated finite element (FE) models, being capable of reproducing the initiation and development of cracks in RC joints. To this end, the uncertainties in material properties, influencing the seismic performance of structural components, are accounted for different joints. In particular, experimentally validated FE models are evolved to stochastic level by generating random variables of material properties with the stratified sampling scheme. Fragility curves representing a certain level of probability of exceeding the defined crack width at the joint back, joint core, and beam-to-joint interface are developed. An application of the proposed methodology for in-situ inspections is also presented, and the results of in-situ measurements of residual crack width from real buildings damaged by recent earthquakes are used for the validation.

The response of a shear critical substandard reinforced concrete beam and the structural repair of the predamaged beam by carbon-fiber-reinforced polymer sheets (CFRPs) were investigated by...

The spatial variability of concrete compressive strength over the specific region is simulated by the stochastic approach. The compressive strength of cores taken from fire-exposed beams simulating non-engineered structures in Turkey is obtained. Therefore, improper mix-design and low strength concrete are targeted. Then, the uneven distribution of the concrete compressive strength along the fire exposed specimens is handled by the random fields approach. Thus, the adverse effect of the fire is not only established by the compressive test results but also the scatter in the core results is estimated by the stochastic method. The stochastic model accounting for the scatter in the core results is generated from the given distribution and mean cylindrical compressive strength values. The concrete compressive strength is not distributed evenly but established stronger and weaker regions over the specimen in the stochastic models. The statistical samples are generated by the Monte Carlo-type stratified sampling method, which is Latin Hypercube Sampling (LHS). A total of 500 random samples closely predicts the scatter in the compressive strength at each temperature level with a range of 25–700 °C. The variability at different geometrical positions on the fire-exposed beam specimens with low strength concrete is characterized as well. Overall, the experimentally observed uncertainties arising from uneven distribution of concrete compressive strength over the fire-exposed beams simulating non-engineered structures in Turkey are accurately reproduced by the random fields approach.

An objective reliability analysis of structural members made of advanced cementitious composites must be based on good knowledge of stochastic properties of individual mechanical fracture parameters of utilized material models. The article presents a comprehensive approach to the design and assessment of precast structural elements including: The series of fracture tests of the two concrete mixtures with various ages in two configurations (three point bending and wedge splitting test, subsequent identification of material parameters using effective crack model, work of fracture method and artificial neural networks, execution of destructive tests of scaled structural members and creation of deterministic models of these tests using collected data. In subsequent phases of the project reliability analysis of tested beams will be carried out in order to obtain stochastic parameters of structural response of prestressed elements to shear load. The obtained data will be used to calibrate the analytical equation describing the response of element exposed to both normal and shear forces. The entire process will be concluded by reliability-based optimization of manufactured components.

The effect of inherent uncertainties in material properties on the global response of beam-type bond-slip specimens constructed from the low strength concrete and plain round bar was investigated by the stochastic study. First, the substandard specimens were tested up to failure under monotonic loading. Bond-slip models, which were derived from the experimental data, were proposed for confined and unconfined RC members with poor concrete quality and plain round bars. Then, the proposed bond-slip relations were implemented in numerical models. The experimentally validated finite element (FE) models were combined with a suitable stochastic sampling technique (Latin Hypercube Sampling (LHS)). Therefore, the variability in the identical tests was characterized by stochastic computational mechanics. The load–displacement curves in the stochastic analysis formed a band around the deterministic model which also simulated the dispersion in the experimental capacity. Besides, the randomized behavior provided the basic statistical parameters of the response quantity. Finally, the partial correlation coefficient between input variables (i.e., material parameters) and response variable was evaluated to outline the material parameters which mainly govern the global response (i.e., sensitivity analysis). It is found that the relative impact of the concrete tensile strength on the ultimate capacity was the most significant.

The uncertainties related to the material properties may significantly affect the seismic perfor-mance of structural components. In this study, the influence of each material propertyon the global response of reinforced concrete substandard beam-column joints is evaluated by means of a sensitivity analysis. An experimentally validated finite element model (FEM) isused for probabilistic numerical simulations. The material propertiesdefined as random variablesare generated by Latin Hypercube Sampling (LHS). The existing correlation among the material parameters is also considered by the simulated annealing approach ingenerating the randomsamples. A set of load-displacement curves is obtained bythe numerical simulations with the randomized material parameters. Theinvestigated outcomes (i.e., responsevariables)are the joint shear strength at the first cracking, the joint peak strength, andjoint strength and crack width at serviceability limit state, which is considered as a repairability threshold value. The basic statistics of the response variablestogether with the probability distribution functions(PDF) are calculated at differentdrift levels. The partial correlation coefficient between mate-rial parameters and response variables is alsoevaluated to outline the parameters which mainly contributetothe joint global response.The reparabilitylevel of the joint isalsoidenti-fied stochasticallyby thecrack width distribution corresponding to the serviceability limit state.

The response of both substandard as-built and CFRP retrofitted RC beam-column joints was investigated by a stochastic study to identify the effect of inherent uncertainties in material constitutive models. Since the scatter of the capacity is inevitably influenced by material properties, the relative impact of each material property on the global response was measured by the sensitivity analysis. It was conducted by evaluating the partial correlation coefficient between material properties and simulated response. First, experimentally validated deterministic nonlinear numerical models were developed in FE environment. After that, they were evolved to the stochastic level, which considers the randomness in prominent material parameters. The basic statistical characteristics and probability density functions of response variables were then provided by the probabilistic assessment. Finally, the most influential material parameters characterizing the quasi-static cyclic behavior of the as-built and retrofitted joints were outlined in accordance with the results of sensitivity analyses. In addition, the hysteric response of the as-built and retrofitted specimens was not only well-characterized by the numerical model but also local damages, such as large diagonal cracks in the as-built specimen and shear cracks after CFRP rupture in the retrofitted specimen, were adequately reproduced in the finite element environment.