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The paper portrays a comprehensive computational procedure for determining the global structural resistances of two existing bridges made of I‐73 and KA‐61 precast post‐tensioned concrete girders using advanced statistical assessment methods in combination with nonlinear fracture mechanics‐based finite element method analysis. Although this combination is a powerful tool for realistic modeling of structures, its practical application is still very time consuming. Therefore, a statistical sampling approach for the determination of the structural design resistance is compared to selected efficient semi‐probabilistic methods based on the estimation of coefficient of variation—estimation of coefficient of variation (ECoV) method according to fib Model Code 2010 and improved approach called Eigen ECoV method. Load‐bearing capacity is determined for the ultimate as well as several serviceability limit states. The sensitivity of the input parameters burdened with uncertainties on the response of the structure is quantified using a sensitivity analysis supported by a surrogate model based on polynomial chaos expansion. The paper shows that the applicability of nonlinear modeling with respect to uncertainties is possible when using these ECoV methods and a surrogate model and can be applied in a routine manner. The shortcomings and advantages of all the used safety design/assessment methods are discussed. [ABSTRACT FROM AUTHOR]

The paper introduces an inverse response surface method utilized when performing reliability-based design optimization of time-consuming problems. Proposed procedure is based on a coupling of the adaptive response surface method and the artificial neural network-based inverse reliability method. The validity and accuracy of the method is tested using examples with explicit nonlinear limit state functions. Obtained results as well as important aspects of the method are discussed.

Structural health monitoring is extremely important for sustaining and preserving the service life of civil structures. Research to identify the damage can detect, locate, quantify and, where appropriate, predict potential structural damage. This paper is about damage identified by non-destructive vibrationbased experiments, which uses the difference between modal frequencies and deflection of an initial and damaged structure. The main objective of this paper is to present a hybrid method for structural damage identification combining artificial neural network and aimed multilevel sampling method. The combination of these approaches yields a more efficient damage identification in terms of time and accuracy of damage localization and damage extent determination. [ABSTRACT FROM AUTHOR]

The paper deals with the experimental and numerical determination of mechanical fracture parameters of fine-grained composites based on the alkali-activated slag (AAS) at different ages of hardening. Two AAS composites, which differed only in the presence of shrinkage reducing admixture, were studied. The prismatic specimens with the nominal dimensions of 40 × 40 × 160 mm and initial central edge notch were subjected to fracture tests in a three-point bending configuration. The results of the fracture tests in the form load F versus deflection d diagrams were used as input data for the identification of parameters via the inverse analysis based on the artificial neural network whose aim is to transfer the fracture test response data to the desired material parameters. The modulus of elasticity, tensile strength, and fracture energy values were identified and subsequently compared with values obtained based on the direct fracture test evaluation using the effective crack model and work-of-fracture method.

The aim of the current research is to develop a complex multilevel approach for the experimental–computational determination of mechanical fracture parameters of concrete as a typical quasi-brittle material. This includes testing, advanced evaluation and soft computing-based identification of specimens of multiple sizes in multiple test configurations and analyses of fracture processes using multiscale modelling approaches. The evaluation of a part of an extensive experimental program is presented in this paper. The basic mechanical fracture parameters of the investigated concrete determined on standard test specimens with nominal dimensions of 100 × 100 × 400 mm subjected to the standard three-point bending fracture test are introduced. The results of three different sets of specimens provided with different depths of the initial notch are compared in terms of absolute values of the selected mechanical fracture parameters. The results indicate different sensitivity of particular mechanical fracture parameters in relation to the depth of the initial notch.

The paper deals with the analysis of the fractal dimension of fracture surfaces of concrete specimens tested in a three-point bending configuration. Fifteen representative specimens were chosen out of a bigger set for the fractal dimension analysis. Their fracture surfaces were scanned by 2D optical profilometry and analysed by the FracDiM software created in the Java programming language. The resulting values of fractal dimensions for specimens of three different sizes are presented.

Structural health monitoring and early detection of structural damage is extremely important to maintain and preserve the service life of civil engineering structures. Identification of structural damage is usually performed using non‐destructive vibration experiments combined with a mathematical procedure called model updating. The finite element model of the investigated structure is updated by incrementally adjusting its parameters so that the model responses gradually approach those of the real possibly damaged structure under investigation. This paper describes the use of two model updating methods. The first method employs metaheuristic optimization technique aimed multilevel sampling to efficiently search the design parameter space to achieve the best match between the deformed structure and its model. The second method approaches model updating as an inverse problem and uses machine learning‐based model to approximate inverse relationship between structural response and structural parameters. Both methods are applied to damage identification of single‐ and double‐span steel trusses. Finally, initial results of the hybrid method are presented. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed. [ABSTRACT FROM AUTHOR]

This paper deals with the assessment of the design resistance of an existing railway bridge made of KT-24 precast post-tensioned concrete girders. The load-bearing capacity of the structure is determined using probabilistic nonlinear analysis by the finite element method. Load-bearing capacity is determined for the ultimate and serviceability limit states. A fully probabilistic approach is compared to selected recommended semi-probabilistic methods, which can greatly reduce the number of nonlinear calculations needed to estimate the design value of resistance. Two stochastic models are compared, reflecting the level of knowledge of actual material properties from the diagnostic survey. The results are compared and discussed with respect to accuracy and required computational time, which is a critical issue when performing a global nonlinear analysis of a complex structure. [ABSTRACT FROM AUTHOR]