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Nayeemuddin Mohammed, Mohammad Ali Khasawneh, Hiren Kumar Mewada, Tasneem Sultana
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Machine learning models for predicting the compressive strength of concrete with shredded PET bottles and M-sand as fine aggregate
10.31436/iiumej.v26i1.2998 · doi-reference
Optimizing cover concrete durability in ready mixed concrete a critical review of curing methods and their impact on performance
10.21741/9781644903414-31 · doi-reference
Analyzing the differences between the impacts of wind and earthquakes on base shear and drift in diagrid structures
10.21741/9781644903414-2 · doi-reference
A comprehensive review on computing methods for the prediction of energy cost in Kingdom of Saudi Arabia
10.21741/9781644903216-21 · doi-reference
A predictive maintenance system for multigranularity faults based on AdaBelief-BP neural network and fuzzy decision making
10.1016/j.aei.2021.101318 · doi-reference
A novel approach to sand production prediction using artificial intelligence
10.1016/j.petrol.2014.07.033 · doi-reference
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
10.1038/s41598-024-65255-2 · doi-reference
Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming
10.1038/s41598-023-39349-2 · doi-reference
Advances in application of machine learning to life cycle assessment: a literature review
10.1007/s11367-022-02030-3 · doi-reference
Sand mining in BRICS economies: tragedy of the commons or fortune in the making?
10.1016/j.jclepro.2023.140122 · doi-reference
Artificial intelligence in supply chain management: a systematic literature review of empirical studies and research directions
10.1016/j.compind.2024.104132 · doi-reference
Design of experiment on concrete mechanical properties prediction: a critical review
10.3390/ma14081866 · doi-reference
Application of machine learning in predicting mechanical properties of sandcrete blocks made from quarry dust: a review
10.1007/s43503-024-00033-7 · doi-reference
The way forward to sustain environmental quality through sustainable sand mining and the use of manufactured sand as an alternative to natural sand
10.1007/s11356-022-19633-w · doi-reference
Assessing riverbank change caused by sand mining and waste disposal using web-based volunteered geographic information
10.3390/w16050734 · doi-reference
The complexity of sand mining in coastal regions of India: implications on livelihoods, marine and riverine environment, sustainable development, and governance
10.54007/ijmaf.2023.e3 · doi-reference
Mechanisms of sand production, prediction–a review and the potential for fiber optic technology and machine learning in monitoring
10.1007/s13202-024-01860-1 · doi-reference
Experimental investigation of sand-based sensible heat energy storage system
10.1016/j.applthermaleng.2024.125390 · doi-reference
Structural design analysis of hybrid multirise buildings, with CLT and LGS light weight floor system, at Neom-The Line
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Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques
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An interpretable XGBoost-SHAP machine learning model for reliable prediction of mechanical properties in waste foundry sand-based eco-friendly concrete
10.1016/j.rineng.2025.104307 · doi-reference
River sand replacement with sustainable sand in design mix concrete for the construction industry
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Suitability of sustainable sand for concrete manufacturing - a complete review of recycled and desert sand substitution
10.1016/j.rineng.2024.102478 · doi-reference
Incorporation of recycled aggregates and silica fume in concrete: an environmental savior-a systematic review
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Evaluation of sisal fiber and aluminum waste concrete blend for sustainable construction using adaptive neuro-fuzzy inference system
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River sand characterization for its use in concrete: a revue
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