Evaluation of statistical parameters of concrete strength from secondary experimental test data

Croce, Pietro; Marsili, Francesca; Klawonn, Frank; Formichi, Paolo; Landi, Filippo

doi:10.1016/j.conbuildmat.2017.11.001

Cited by 19 publications

(22 citation statements)

References 26 publications

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“…To obtain a proper evaluation of the vulnerability of existing masonry buildings and to design efficient strengthening interventions, not only are reliable analysis methods necessary, but also refined characterization of the seismic behavior of masonry walls is needed. In that perspective, accurate estimations of mechanical parameters are essential to obtain a consistent classification of seismic performance by means of a seismic risk index (Croce et al 2018c;Beconcini et al 2019). Note: Bold values are used for values in the diagonal of the matrix and correspond to the probability of having the same class identification with the two methods.…”

Section: Updating the Capacity Curves Of Masonry Wallsmentioning

confidence: 99%

Bayesian Methodology for Probabilistic Description of Mechanical Parameters of Masonry Walls

Croce

Beconcini

Formichi

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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In consideration of the high vulnerability of the built environment, the assessment of seismic behavior of existing masonry buildings is a key topic in view of their retrofitting and reuse. Because masonry's behavior depends on complex nonhomogeneous, anisotropic, asymmetric, and nonlinear properties, the definition of suitable mechanical models is still a critical issue, especially for stone masonry. Structural analyses of existing masonry buildings in seismic-prone areas are thus significantly influenced by the adopted mechanical models and assumptions about their relevant masonry properties, which are characterized by large uncertainty. In this study, a procedure for the definition of masonry classes and probability density functions of relevant mechanical parameters, such as elastic modulus and shear modulus, is proposed. The general procedure is illustrated referring to a significant number of in situ double-flat-jack test results on stone masonry obtained by the authors during an ad hoc experimental campaign. Finally, combining information on masonry quality obtained by visual inspection with results of in situ tests, a Bayesian methodology is proposed for the updating of masonry mechanical parameters, thereby providing the basis for a more refined probabilistic assessment of the seismic risk index.

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Section: Updating the Capacity Curves Of Masonry Wallsmentioning

confidence: 99%

Bayesian Methodology for Probabilistic Description of Mechanical Parameters of Masonry Walls

Croce

Beconcini

Formichi

et al. 2021

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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“…The experimental results, supplemented with relevant literature data, allowed for the setup of a consistent database of mechanical properties of various masonry types, from which it was possible to derive a rational classification. At the same time, adopting a general procedure, based on cluster analysis of raw data, successfully proposed by the authors to identify homogenous populations within large dataset [ 20 ], sound information about statistical properties of relevant investigated mechanical parameters was obtained.…”

Section: Mechanical Behavior Of Masonry Wallsmentioning

confidence: 99%

Experimental Evaluation of Shear Behavior of Stone Masonry Wall

et al. 2021

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The evaluation of the shear behavior of masonry walls is a first fundamental step for the assessment of existing masonry structures in seismic zones. However, due to the complexity of modelling experimental behavior and the wide variety of masonry types characterizing historical structures, the definition of masonry’s mechanical behavior is still a critical issue. Since the possibility to perform in situ tests is very limited and often conflicting with the needs of preservation, the characterization of shear masonry behavior is generally based on reference values of mechanical properties provided in modern structural codes for recurrent masonry categories. In the paper, a combined test procedure for the experimental characterization of masonry mechanical parameters and the assessment of the shear behavior of masonry walls is presented together with the experimental results obtained on three stone masonry walls. The procedure consists of a combination of three different in situ tests to be performed on the investigated wall. First, a single flat jack test is executed to derive the normal compressive stress acting on the wall. Then a double flat jack test is carried out to estimate the elastic modulus. Finally, the proposed shear test is performed to derive the capacity curve and to estimate the shear modulus and the shear strength. The first results obtained in the experimental campaign carried out by the authors confirm the capability of the proposed methodology to assess the masonry mechanical parameters, reducing the uncertainty affecting the definition of capacity curves of walls and consequently the evaluation of seismic vulnerability of the investigated buildings.

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“…The compressive strength is a key issue but it is a random variable that can vary over the time, even in a single structure [40]. To enhance professional practice an accurate and appropriate classification of compressive concrete strength is required [41].…”

Section: Output Variable: Compressive Strengthmentioning

confidence: 99%

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

Tenza-Abril

Satorre-Cuerda

Compañ

et al. 2019

Computational Methods and Experimental Measurements XIX

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Lightweight aggregates (LWA) are used to produce low-density concretes required for building applications. Lightweight aggregate concrete (LWAC) is a multipurpose material for construction, which offers technical, economical and environment benefits, and it is produced by replacing the normal-weight aggregates with LWA, depending upon the requirements of density and strength. LWAC is a complex composite material, and a model of its compressive strength must be highly nonlinear because it is very sensitive to its ingredients, so modelling its behaviour is a difficult task. Many studies have tried to develop accurate and effective predictive models for LWAC compressive strength. In this study, a support vector machine (SVM) learning algorithm is used to propose a model to classify the compressive strength of a wide range of LWAC. A dataset of 241 different LWACs were used for classifying the compressive strength into six different classes (from low-strength to high-strength) using different variables-quantity of cement, water and LWA in the dosage and density of the LWAC produced. The results show that increasing the variables means the model becomes more accurate up to approximately an 80% rate of success. The SVM model proved to be a significant tool to classify the compressive strength of LWAC contributing to engineers avoiding costly experimental trial tests.

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Evaluation of statistical parameters of concrete strength from secondary experimental test data

Cited by 19 publications

References 26 publications

Bayesian Methodology for Probabilistic Description of Mechanical Parameters of Masonry Walls

Bayesian Methodology for Probabilistic Description of Mechanical Parameters of Masonry Walls

Experimental Evaluation of Shear Behavior of Stone Masonry Wall

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

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