ArticleEnvironmental science and pollution research international2021
Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Parametric optimization of the slot waveguide characteristics using a machine-learning approach.Scientific reports · 2025Article
- Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence.3D printing and additive manufacturing · 2024Article
- Deep learning-based prediction of compressive strength of eco-friendly geopolymer concrete.Environmental science and pollution research international · 2024Article
- A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN.Sensors (Basel, Switzerland) · 2024Article
- Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.PeerJ. Computer science · 2024Article
- Integrative soft computing approaches for optimizing thermal energy performance in residential buildings.PloS one · 2023Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
One of the most significant parameters in concrete design is compressive strength. Time and money could be saved if the compressive strength of concrete is accurately measured. In this study, two machine learning models, namely, boosted decision tree regression (BDTR) and support vector machine (SVM), were developed to predict concrete compressive strength (CCS) using a complete dataset through the previous scientific studies. Eight concrete mixture parameters were used as the input dataset. Four statistical indices, namely the coefficient of determination (R
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Registered trials
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