Literature DB >> 34300748

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

Muhammad Sufian1, Safi Ullah1, Krzysztof Adam Ostrowski2, Ayaz Ahmad2,3, Asad Zia4, Klaudia Śliwa-Wieczorek2, Muhammad Siddiq1, Arsam Ahmad Awan5.   

Abstract

Marble is currently a commonly used material in the building industry, and environmental degradation is an inevitable consequence of its use. Marble waste occurs during the exploitation of deposits using shooting technologies. The obtained elements most mainly often have an irregular geometry and small dimensions, which excludes their use in the stone industry. There is no systematic way of disposing of these massive mounds of waste, which results in the occurrence of landfills and environmental pollution. To mitigate this problem, an effort was made to incorporate waste marble powder into clay bricks. Different percentage proportions of marble powder were considered as a partial substitute for clay, i.e., 5-30%. A total of 105 samples were prepared in order to assess the performance of the prepared marble clay bricks, i.e., their water absorption, bulk density, apparent porosity, salt resistance, and compressive strength. The obtained bricks were 1.3-19.9% lighter than conventional bricks. The bricks with the addition of 5-20% of marble powder had an adequate compressive strength with regards to the values required by international standards. Their compressive strength and bulk density decreased, while their water absorption capacity and porosity improved with an increased content of marble powder. The obtained empirical equations showed good agreement with the experimental results. The use of waste marble powder in the construction industry not only lowers project costs, but also reduces the likelihood of soil erosion and water contamination. This can be seen to be a crucial factor for economic growth in agricultural production.

Entities:  

Keywords:  bricks; clay; compressive strength; eco-friendly materials; marble powder; marble waste

Year:  2021        PMID: 34300748     DOI: 10.3390/ma14143829

Source DB:  PubMed          Journal:  Materials (Basel)        ISSN: 1996-1944            Impact factor:   3.623


  11 in total

1.  Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.

Authors:  Kaffayatullah Khan; Waqas Ahmad; Muhammad Nasir Amin; Ayaz Ahmad; Sohaib Nazar; Majdi Adel Al-Faiad
Journal:  Polymers (Basel)       Date:  2022-06-20       Impact factor: 4.967

2.  Assessment of Limestone Waste Addition for Fired Clay Bricks.

Authors:  Gyorgy Thalmaier; Nicoleta Cobȋrzan; Anca-Andreea Balog; Horia Constantinescu; Andrei Ceclan; Mirela Voinea; Traian Florin Marinca
Journal:  Materials (Basel)       Date:  2022-06-16       Impact factor: 3.748

3.  Evaluation of Artificial Intelligence Methods to Estimate the Compressive Strength of Geopolymers.

Authors:  Yong Zou; Chao Zheng; Abdullah Mossa Alzahrani; Waqas Ahmad; Ayaz Ahmad; Abdeliazim Mustafa Mohamed; Rana Khallaf; Samia Elattar
Journal:  Gels       Date:  2022-04-26

4.  Comparison of Prediction Models Based on Machine Learning for the Compressive Strength Estimation of Recycled Aggregate Concrete.

Authors:  Kaffayatullah Khan; Waqas Ahmad; Muhammad Nasir Amin; Fahid Aslam; Ayaz Ahmad; Majdi Adel Al-Faiad
Journal:  Materials (Basel)       Date:  2022-05-10       Impact factor: 3.748

5.  Compressive Strength Estimation of Geopolymer Composites through Novel Computational Approaches.

Authors:  Muhammad Nasir Amin; Kaffayatullah Khan; Waqas Ahmad; Muhammad Faisal Javed; Hisham Jahangir Qureshi; Muhammad Umair Saleem; Muhammad Ghulam Qadir; Muhammad Iftikhar Faraz
Journal:  Polymers (Basel)       Date:  2022-05-23       Impact factor: 4.967

6.  Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete.

Authors:  Xiongzhou Yuan; Yuze Tian; Waqas Ahmad; Ayaz Ahmad; Kseniia Iurevna Usanova; Abdeliazim Mustafa Mohamed; Rana Khallaf
Journal:  Materials (Basel)       Date:  2022-04-12       Impact factor: 3.748

7.  Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials.

Authors:  Costel Anton; Silvia Curteanu; Cătălin Lisa; Florin Leon
Journal:  Materials (Basel)       Date:  2021-11-26       Impact factor: 3.623

8.  Application of Machine Learning Approaches to Predict the Strength Property of Geopolymer Concrete.

Authors:  Rongchuan Cao; Zheng Fang; Man Jin; Yu Shang
Journal:  Materials (Basel)       Date:  2022-03-24       Impact factor: 3.623

9.  Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms.

Authors:  Meijun Shang; Hejun Li; Ayaz Ahmad; Waqas Ahmad; Krzysztof Adam Ostrowski; Fahid Aslam; Panuwat Joyklad; Tomasz M Majka
Journal:  Materials (Basel)       Date:  2022-01-15       Impact factor: 3.623

10.  Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites.

Authors:  Qichen Wang; Waqas Ahmad; Ayaz Ahmad; Fahid Aslam; Abdullah Mohamed; Nikolai Ivanovich Vatin
Journal:  Polymers (Basel)       Date:  2022-03-08       Impact factor: 4.329

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