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Innovation in Construction Materials: Development of Ecological Concrete Blocks with Expanded Polystyrene Integrating Design of Experiments and Machine Learning

https://doi.org/10.2139/ssrn.4866077
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/app.47529openalex-extracted
Application of expanded polystyrene (EPS) in buildings and constructions: A review
inferredcites →https://doi.org/10.1016/j.compstruct.2021.113917openalex-extracted
Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites
inferredcites →https://doi.org/10.1016/j.conbuildmat.2019.117829openalex-extracted
Ductility analysis of vegetal-fiber reinforced raw earth concrete by mixture design
inferredcites →https://doi.org/10.1016/j.conbuildmat.2020.119158openalex-extracted
Exploring the full replacement of cement with expanded polystyrene (EPS) waste in mortars used for masonry construction
inferredcites →https://doi.org/10.1016/j.conbuildmat.2020.119889openalex-extracted
Machine learning prediction of mechanical properties of concrete: Critical review
inferredcites →https://doi.org/10.1016/j.conbuildmat.2020.120286openalex-extracted
Machine learning prediction of compressive strength for phase change materials integrated cementitious composites
inferredcites →https://doi.org/10.1016/j.gaitpost.2013.09.001openalex-extracted
Ellipse area calculations and their applicability in posturography
inferredcites →https://doi.org/10.1016/j.istruc.2022.12.108openalex-extracted
Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques
inferredcites →https://doi.org/10.1016/j.jclepro.2023.138673openalex-extracted
Machine learning models for predicting compressive strength of fiber-reinforced concrete containing waste rubber and recycled aggregate
inferredcites →https://doi.org/10.1016/j.jobe.2022.104746openalex-extracted
Predictive modelling of sustainable lightweight foamed concrete using machine learning novel approach
inferredcites →https://doi.org/10.1016/j.jobe.2023.108065openalex-extracted
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
inferredcites →https://doi.org/10.1016/j.matpr.2021.10.503openalex-extracted
Lightweight concrete blocks produced using expanded polystyrene and foaming agent
inferredcites →https://doi.org/10.1016/j.neucom.2008.02.002openalex-extracted
Design of experiments on neural network's training for nonlinear time series forecasting
inferredcites →https://doi.org/10.1016/j.rineng.2023.101390openalex-extracted
Optimization of colloidal nano-silica based cementitious mortar composites using RSM and ANN approaches
inferredcites →https://doi.org/10.1016/j.rser.2017.04.027openalex-extracted
A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment
inferredcites →https://doi.org/10.1038/s43586-022-00184-wopenalex-extracted
Principal component analysis
inferredcites →https://doi.org/10.1080/00224065.1988.11979093openalex-extracted
Programs for Generating Extreme Vertices and Centroids of Linearly Constrained Experimental Regions
inferredcites →https://doi.org/10.1080/00401706.1966.10490377openalex-extracted
Extreme Vertices Design of Mixture Experiments
inferredcites →https://doi.org/10.1080/00401706.1974.10489209openalex-extracted
Extreme Vertices Designs for Linear Mixture Models
inferredcites →https://doi.org/10.1080/00401706.1993.10484984openalex-extracted
A Systematic Approach to Planning for a Designed Industrial Experiment
inferredcites →https://doi.org/10.1080/10298436.2021.2020785openalex-extracted
Characterisation of foam bitumen mixes with diffrent RAP content at elevated mixing temperature using design of experiment (DOE) approach
inferredcites →https://doi.org/10.1081/qen-120003561openalex-extracted
Experimental Designs for Constrained Regions
inferredcites →https://doi.org/10.1109/tpami.2017.2691703openalex-extracted
Leave-One-Out Kernel Optimization for Shadow Detection and Removal
inferredcites →https://doi.org/10.1287/ijoc.1050.0136openalex-extracted
State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments
inferredcites →https://doi.org/10.1371/journal.pone.0168288openalex-extracted
Generalising Ward’s Method for Use with Manhattan Distances
inferredcites →https://doi.org/10.3390/ma14081866openalex-extracted
Design of Experiment on Concrete Mechanical Properties Prediction: A Critical Review
inferredcites →https://doi.org/10.3390/ma14123143openalex-extracted
Predicting Mechanical Properties of High-Performance Fiber-Reinforced Cementitious Composites by Integrating Micromechanics and Machine Learning
inferredcites →https://doi.org/10.3390/polym14081583openalex-extracted
Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF
inferredcites →https://doi.org/10.2307/1269280openalex-snapshot
A Systematic Approach to Planning for a Designed Industrial Experiment
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