[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Chen et al., 2023 - Google Patents

Predictive modelling for the acid resistance of cement-based composites modified with eggshell and glass waste for sustainable and resilient building materials

Chen et al., 2023

Document ID
768116651821519586
Author
Chen Z
Amin M
Iftikhar B
Ahmad W
Althoey F
Alsharari F
Publication year
Publication venue
Journal of Building Engineering

External Links

Snippet

The increasing demand for cement-based composites (CBCs) due to the advancement of infrastructure causes the exhaustion of natural materials and environmental pollution. Also, dumping industrial and agro-derived waste materials in landfills has negative impacts …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology

Similar Documents

Publication Publication Date Title
Chen et al. Predictive modelling for the acid resistance of cement-based composites modified with eggshell and glass waste for sustainable and resilient building materials
Chu et al. Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete
Shahmansouri et al. Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method
Nazar et al. Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer
Bui et al. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete
Shahmansouri et al. The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network
Nguyen et al. Artificial intelligence algorithms for prediction and sensitivity analysis of mechanical properties of recycled aggregate concrete: A review
Wang et al. Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Kuang et al. Application of back propagation neural network to the modeling of slump and compressive strength of composite geopolymers
Pazouki Fly ash-based geopolymer concrete's compressive strength estimation by applying artificial intelligence methods
Piro et al. Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement
Amin et al. Evaluating the relevance of eggshell and glass powder for cement-based materials using machine learning and SHapley Additive exPlanations (SHAP) analysis
Faraz et al. A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin
Alabduljabbar et al. Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniques
Dash et al. Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature
Karthiyaini et al. Prediction of mechanical strength of fiber admixed concrete using multiple regression analysis and artificial neural network
Zheng et al. Sustainable predictive model of concrete utilizing waste ingredient: Individual alogrithms with optimized ensemble approaches
Gogineni et al. Predicting compressive strength of concrete with fly ash and admixture using XGBoost: a comparative study of machine learning algorithms
Cao et al. A soft-computing-based modeling approach for predicting acid resistance of waste-derived cementitious composites
Verma Prediction of compressive strength of geopolymer concrete by using ANN and GPR
Almohammed et al. Flexural and split tensile strength of concrete with basalt fiber: An experimental and computational analysis
Rosa et al. Use of operational research techniques for concrete mix design: A systematic review
Alarfaj et al. Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete
Nazari et al. Compressive strength of tungsten mine waste-and metakaolin-based geopolymers
Zhang et al. Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies