Compressive Strength of Concrete Masonry Prisms

Cheema, Tariq S.; Klinger, R E

doi:10.14359/1752

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…Where, CE and CR are the coefficient of variations for effects (E) and resistance (R). Then the ratio of R and E can be determined from the function given in Eq (7), Where En and Rn are the normalised effect and resistance parameters. γ and ϕ are the partial safety and capacity reduction factors.…”

Section: Reliability Indices Of Proposed Correlationsmentioning

confidence: 99%

“…For designing of these walls, masonry compressive strength is of primary importance. In grouted masonry, the strength and deformation properties of grout also influence the compressive strength, along with other parameters mentioned before [7][8][9]. Therefore, determination of compressive strength of grouted masonry is more complex than un-grouted masonry.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been conducted to assess the influence of grout strengths to the compressive strengths of grouted concrete masonry in the last four decades [7,[10][11][12]. Drysdale and Hamid [12], investigated the compressive strengths of grouted concrete masonry by varying block, mortar and grout properties, and a similar study was recently conducted by Martins et al [13].…”

Section: Introductionmentioning

confidence: 99%

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Reliable unit strength correlations to predict the compressive strength of grouted concrete masonry

Thamboo,

Sathurshan,

Zahra

2024

Preprint

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Compressive strength of grouted concrete masonry is an important parameter to design reinforced/grouted concrete masonry walls. The design standards stipulate two methods to determine the compressive strength of masonry (1) using tabulated unit strength and mortar type, and (2) testing representative masonry prisms. The compressive strength prediction of grouted concrete masonry is influenced by compressive strength characteristics of hollow blocks, mortar and grout used. Due to insufficient test data, research in this area is limited. Therefore, a multi-level approach was employed in this study to improve the existing unit strength correlations of the standards for more accurate prediction of compressive strengths of grouted concrete masonry. Initially, the existing methods to determine the compressive strength of grouted masonry were critically appraised and a database of compression tests of grouted concrete masonry prisms/wallettes was developed. This database was then used to evaluate the correlations between the compressive strengths of block, mortar, grout and masonry. The applicability of existing unit strength correlations from the design standards and literature were assessed and their relevancy and limitations are highlighted. Subsequently, updated sets of unit strength correlations are proposed in this study, through statistical reliability analyses of the predictions against the experimental results included in the database. The proposed unit strength correlations were classified according to the mortar type/strengths (≤ 10 MPa and > 10 MPa). It has been shown that the new correlations are more reliable than the existing unit strength correlations through comparing the 95th percentile error values.

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Section: Reliability Indices Of Proposed Correlationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliable unit strength correlations to predict the compressive strength of grouted concrete masonry

Thamboo,

Sathurshan,

Zahra

2024

Preprint

View full text Add to dashboard Cite

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“…In this study, 102 hollow concrete specimens were derived from the literature [29,44,[48][49][50][51][52][53][54][55]. The database includes three input variables, including the compressive strength of mortar (f m ), the compressive strength of blocks (f b ), height-to-thickness ratio (h/t), the ratio of f m /f b and the output is the compressive strength of prisms (CS).…”

Section: Data Collection and Description Of The Databasementioning

confidence: 99%

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm

Ho,

Tran

2024

PLoS ONE

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The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques. This study employed Gradient Boosting (GB) to evaluate and predict the CS of hollow masonry prism. The database consists of 102 hollow concrete specimens taken from different previous published literature used for modeling. The output is the CS of the hollow masonry prism, while the inputs include the compressive strength of mortar (fm), the compressive strength of blocks (fb), height-to-thickness ratio (h/t), the ratio of fm/fb. To reduce the overfitting problem, this study used K-Fold cross-validation, then particle swarm optimization (PSO) was employed to obtain the optimum hyperparameter. The GB model then was modeled using the optimum hyperparameters. The results showed that the GB model performed very well in evaluating and predicting the CS of the hollow masonry prims with a high prediction accuracy, the values of R2, RMSE, MAE, and MAPE are 0.977, 0.803 MPa, 0.612 MPa, and 0.036%, respectively. The performance of the GB model in this study outperformed in comparison to six different machine learning models (decision tree, linear regression, random forest regression, ridge regression, Artificial Neural network, and Extreme Gradient Boosting) used in previous studies. The results of sensitivity analysis using SHAP and PDP-2D indicate that the CS is strongly dependent on the fb (with a mean SHAP value of 3.2), h/t (with a mean SHAP value of 1.63), while the fm/fb (with a mean SHAP value of 0.57) had a small effect on the CS. Thus, it can be stated that this research provides a good method to evaluate and predict the CS of the hollow masonry prism, which can bring good knowledge for practical application in this field.

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“…To supplement the practing and examing datasets, 90 test data sets comprising test results for more than 300 prototypes were collected [16,17] and published literature [18]. The data were chosen according to the following criterion to guarantee conformity with specifications and building practices:…”

Section: Data Collectionmentioning

confidence: 99%

Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Muthukumar,

Jegatheeswaran

2024

Matéria (Rio J.)

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When the only information available is the issue parameters, and the intended outwards, machine learning techniques like ANN (Artificial Neural Networks) and ANFIS (Adaptive neuro-fuzzy inference system) been proven to address the complex problems without duplicating the phenomena under investigation. The main prompting characteristics are the height-to-the-thicknesses ratio of prisms and the strength under compression of prisms and mortar were analyzed. As inputs, the prototypes are used as blocks and mortars. Both prototypes were accomplished and evaluated. Thirty-six data sets were gathered for testing in addition to verified technical and subsequently comparison with other empirical computation methods served to validate. The outwards show that the suggested prototypes have good forecast capabilities with negligible error rates. To assess and compare the structural behavior of structural completion of AAC block with the other types. At last, both the machine learning tools are good application and dependability.

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Compressive Strength of Concrete Masonry Prisms

Cited by 10 publications

References 2 publications

Reliable unit strength correlations to predict the compressive strength of grouted concrete masonry

Reliable unit strength correlations to predict the compressive strength of grouted concrete masonry

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm

Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

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