Multifactorial experimental analysis of concrete compressive strength as a function of time and water-to-cement ratio

Gavela, Stamatia; Nikoloutsopoulos, Nikolaos; Papadakos, George; Passa, Dimitra; Sotiropoulou, Anastasia

doi:10.1016/j.prostr.2018.09.020

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…The time/temperature-compressive strength relationships were often exponential. 67,68 The general form of the analytical model-developed based on these concepts/notionsis shown in Equation ( 3), which consists of eight input variables. Here, CS is compressive strength (MPa); X FA , X SS , and X SH are normalized mass fraction (unitless) of…”

Section: Analytical Model Development To Predict Compressive Strengthmentioning

confidence: 99%

Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

et al. 2022

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Alkali-activated mortar (AAM) is an emerging eco-friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs-albeit much needed to complement experiments-is difficult, owing to substantive batch-to-batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the modelonce trained and optimized-can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e., network ratio and number of constraints); thus, reducing the degrees of freedom. Through interpretation of the ML model's outcomes-specifically the variable importance for the AAMs' compressive strength-a simple, easy-to-use, closed-form analytical model is developed. Results demonstrate that the analytical model yields predictions of compressive strength of AAMs without scarifying much accuracy compared to the ML model. Overall, this study's outcomes demonstrate a roadmap-incorporates composite chemostructural descriptors in ML models-that can be employed to design AAMs to achieve targeted compressive strength.

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Section: Analytical Model Development To Predict Compressive Strengthmentioning

confidence: 99%

Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

et al. 2022

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“…Mathematical models that value this relationship for a given specimen curing age have been early proposed [2,3]. Recent work has suggested mathematical models describing the apparent association of the specimen compressive strength with curing age [4][5][6][7][8][9][10][11]. Another factor that affects the result of concrete compressive strength for a specimen of a given curing age is the temperature of the environment in which the curing process takes place [5].…”

Section: Uncertainty Parameters Identification and Analysismentioning

confidence: 99%

“…For this reason, the measurement uncertainty for the reference standard should always be incorporated into the uncertainty budget of a test according to EN 12390-3. All of the above are summarized in the cause-and-effect diagram (Ishikawa diagram, as already been introduced by authors of this paper in a previous study [8]) which has been used according to guidance from EURACHEM [11], in a way [8,12] that aims at mapping uncertainty factors while simultaneously visualizing synergies between them ( Fig.1). T Figure 1: Cause-and-effect diagram on how uncertainty factors contribute to the final result of a test according to EN 12390-3.…”

Section: Uncertainty Parameters Identification and Analysismentioning

confidence: 99%

“…For this reason in the frame of an extended study aiming at the creation of a function that correlates the testing result on the compressive strength of concrete specimens to all the significant of the above parameters, only the experimental investigation of the correlation of compressive strength testing results with the parameters of curing age and water to cement ratio was examined. The integration of various similar experiments of such a protocol by various laboratories and for various parameters of the test procedure could speed the achievement of a standardized semi-empirical model on the relation of concrete compressive strength as a function of a great number of testing parameters and mix materials characteristics [8].…”

Section: Scope Of Experiments IImentioning

confidence: 99%

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Experimental uncertainty budget for concrete compressive strength test based on a multifactorial analysis

Gavela

Nikoloutsopoulos

Papadakos

et al. 2019

Frattura Ed Integrità Strutturale

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The objective of the study is to introduce an experimental uncertainty budget process for concrete compressive strength test, based on a protocol that incorporates effects of multiple factors significant for the measurement result. The proposed procedure is rather useful for laboratories seeking accreditation according to ISO/IEC 17025, in order to emphasize the contribution of type A uncertainty estimations, rather than relying on type B estimations that are unable to address the correlation between those factors. Two independent experiments were performed. Experiment I is proposed as a simple, suitably designed, reproducibility trial for laboratories performing EN 12390 test method, i.e. when a specified nominal curing age is targeted, following experimental design on multiple uncertainty parameters. A sensitivity analysis was introduced based on a semi-empirical multifactorial regression model (experiment II) for concrete compressive strength as a function of specimen's curing age and W/C ratio. The present study is an effort towards an integrated and standardized method for experimental, semi-empirical multifactorial regression estimation of the uncertainty budget for the EN 12390 test method, being useful, also, as a baseline for internal quality control programs when adjusted for the specific characteristics of concrete specimens tested by a laboratory.

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“…The bulk of concrete is made up of fine and coarse aggregates (crushed or gravel) and therefore concrete properties are significantly influenced by the aggregate type and granulation [ 2 , 3 , 4 ]. However, the hydrated cement matrix also affects concrete strength [ 5 , 6 ]. It should be pointed that the internal structure of hardened concrete consists of aggregate grains and cement matrix as well as the initial system of defects like microcracks, unhydrated cement grains, voids and pores.…”

Section: Introductionmentioning

confidence: 99%

Length Effect at Testing Splitting Tensile Strength of Concrete

Słowik

Akram

2021

Materials

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Tensile strength of concrete is the basic property when estimating the cracking resistance of the structure and when analysing fracture processes in concrete. The most common way of testing tensile strength is the Brazilian method. It has been noticed that the shape and size of specimens influence the tensile splitting strength. The experiments were performed to investigate the impact of cylinder’s length on tensile concrete strength received in the Brazilian method. During the experiment the tensile concrete strength was tested on two different sizes cylindrical specimens: 150 mm × 150 mm and 150 mm × 300 mm. Experiments were performed in two stages, with two types of maximum aggregate size: 16 mm and 22 mm. The software “Statistica” was used to perform the broad scale statistical analysis. When comparing test results for shorter and longer specimens, the increase of tensile splitting strength tested on shorter cylinders was observed (approximately 5%). However, when performing deeper statistical analysis, it has been found that the length effect was not sensitive to the strength of the cement matrix and the type of aggregate but was influenced by the aggregate size. Further experiments are needed in order to perform a multi-parameter statistical analysis of scale effect when testing the splitting tensile strength of concrete.

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Multifactorial experimental analysis of concrete compressive strength as a function of time and water-to-cement ratio

Cited by 9 publications

References 6 publications

Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

Machine learning enabled closed‐form models to predict strength of alkali‐activated systems

Experimental uncertainty budget for concrete compressive strength test based on a multifactorial analysis

Length Effect at Testing Splitting Tensile Strength of Concrete

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