Indirect assessment of concrete resistance from FE model updating and Young’s modulus estimation of a multi-span PSC viaduct: Experimental tests and validation

Aloisio, Angelo; Pasca, Dag Pasquale; Battista, Luca Di; Rosso, Marco Martino; Cucuzza, Raffaele; Marano, Giuseppe Carlo; Alaggio, Rocco

doi:10.1016/j.istruc.2022.01.045

Cited by 23 publications

(11 citation statements)

References 43 publications

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“…Determining the compressive, flexural, and tensile strengths is a time-consuming task and requires machinery and personnel. These properties are of interest to engineers and concrete makers onsite especially compressive strength because it defines the concrete grades (Ziolkowski and Niedostatkiewicz, 2019;Aloisio et al, 2022). ML has been used to predict the compressive strength based on the input of mix design (Chou et al, 2014;Abuodeh et al, 2020;Chaabene et al, 2020).…”

Section: Prediction Of Mechanical Propertiesmentioning

confidence: 99%

Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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Machine learning techniques have been used in different fields of concrete technology to characterize the materials based on image processing techniques, develop the concrete mix design based on historical data, and predict the behavior of fresh concrete, hardening, and hardened concrete properties based on laboratory data. The methods have been extended further to evaluate the durability and predict or detect the cracks in the service life of concrete, It has even been applied to predict erosion and chemical attaches. This article offers a review of current applications and trends of machine learning techniques and applications in concrete technology. The findings showed that machine learning techniques can predict the output based on historical data and are deemed to be acceptable to evaluate, model, and predict the concrete properties from its fresh state, to its hardening and hardened state to service life. The findings suggested more applications of machine learning can be extended by utilizing the historical data acquitted from scientific laboratory experiments and the data acquitted from the industry to provide a comprehensive platform to predict and evaluate concrete properties. It was found modeling with machine learning saves time and cost in obtaining concrete properties while offering acceptable accuracy.

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Section: Prediction Of Mechanical Propertiesmentioning

confidence: 99%

Machine learning in concrete technology: A review of current researches, trends, and applications

Gamil

2023

Front. Built Environ.

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“…This can be solved by subdividing the structure in members that are dealt with independently and to which different homogeneous fields can be assigned, as e.g. indicated in (Aloisio et al, 2022). Since concrete structures are produced according to quality control processes, the variation in mean value along (elements of) the structure can be considered small and the use of a homogeneous field can be justified in case of non-degrading structures (Tran et al, 2012).…”

Section: Definitionmentioning

confidence: 99%

Random field modelling of spatial variability in concrete – a review

Botte,

Vereecken,

Caspeele

2023

Structure and Infrastructure Engineering

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Concrete is a heterogeneous material, consisting of different constituents such as aggregates of different sizes and cement paste. Due to the heterogeneity of the concrete, its properties such as strength, stiffness, permeability, etc. show variations in space. In scientific literature, several investigations have shown the influence of spatial variation of material properties on the reliability of reinforced concrete structures, and it was concluded that spatial variability and spatial correlation should be accounted for to obtain accurate estimations of e.g. the load-bearing capacity of degrading concrete structures.For this purpose, random fields are often applied in literature. To apply these random fields in a structural reliability analysis, a correlation model and corresponding correlation length need to be defined. Nevertheless, experimental results are not always available to derive the correlation model, and hence some modelling assumptions are required. However, there is no consensus yet in literature on which correlation model to apply for the different concrete properties. This paper provides an overview of different correlation models applied in literature in relation to the analysis of reinforced concrete structures.

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“…This is realized by adopting specific pseudo-random-based operators such as crossover, mutation, and selection in order to reproduce the long-term process of evolution in a population with the survival of the fittest individuals [2]. In the last two decades, the adoption of metaheuristic algorithms in many engineering applications highlighted their successful capabilities to deal with realworld constrained problems [3][4][5][6][7][8], e.g., dealing with structural design [9][10][11][12] and structural optimization tasks [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

et al. 2022

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Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method.

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Indirect assessment of concrete resistance from FE model updating and Young’s modulus estimation of a multi-span PSC viaduct: Experimental tests and validation

Cited by 23 publications

References 43 publications

Machine learning in concrete technology: A review of current researches, trends, and applications

Machine learning in concrete technology: A review of current researches, trends, and applications

Random field modelling of spatial variability in concrete – a review

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

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