An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material

Aslam

Martínez‐García

et al. 2021

Sci Rep

Industrial waste has been rapidly increased day by day because of the fast-growing population which results environmental pollutions. It has been recommended that the disposal of industrial waste would be greatly reduced if it could be incorporated in concrete production. In cement concrete technology, there are many possibilities to use waste materials either as cement replacement or aggregate in concrete production. Two major industrials waste are glass and marble waste. The basic objective of this investigation is to examine the characteristics of concrete waste glass (WG) as binding material in proportions 10%, 20% and 30% by weight of cement. Furthermore, to obtain high strength concrete, waste marble in proportion of 40%, 50% and 60% by weight cement as fine aggregate were used as a filler material to fill the voids between concrete ingredients. Fresh properties were evaluated through slump cone test while mechanical performance was evaluated through compressive strength and split tensile strength which were performed after 7 days, 28 days and 56 days curing. Results show that, workability of concrete decreased with incorporation of waste glass and marble waste. Furthermore, mechanical performance improved considerably up 20% and 50% substitution of waste glass and waste marble respectively. Statistical approach of Response Surface Methodology (RSM) was used optimize both waste materials in concrete. Results indicate better agreement between statistical and experimental results.

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Effects of waste glass and waste marble on mechanical and durability performance of concrete

Aslam

Martínez‐García

et al. 2021

Sci Rep

“…To produce the predicted output variable, SML algorithms need various input variables [ 33 , 34 ]. The data utilized in this work to forecast the CS of concrete using FA and BFS as SCM were derived from the literature [ 35 , 36 , 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Ostrowski

et al. 2021

Materials

Self Cite

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.

“…ML techniques require a variety of input variables to produce the expected output [36,37]. The data used to forecast the C-S of HPC were retrieved from the literature [38][39][40][41].…”

Section: Data Descriptionmentioning

confidence: 99%

Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

et al. 2021

Materials

Self Cite

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.