Machine learning prediction of compressive strength for phase change materials integrated cementitious composites

Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold

(2019) Debaditya Chakraborty et al.   ENERGY AND BUILDINGS

Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model

(2019) Behrooz Keshtegar et al.   COMPOSITE STRUCTURES

Multiple mechanical properties prediction of hydraulic concrete in the form of combined damming by experimental data mining

(2019) Mengxi Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees

(2019) Yingjiu Pan et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression

(2019) Junfei Zhang et al.   CONSTRUCTION AND BUILDING MATERIALS

Integrating phase change materials in construction materials: Critical review

(2019) Afshin Marani et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting reinforcing bar development length using polynomial chaos expansions

(2019) Zaher Mundher Yaseen et al.   ENGINEERING STRUCTURES

Compressive strength of Foamed Cellular Lightweight Concrete simulation: New development of hybrid artificial intelligence model

(2019) Ali Ashrafian et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

(2018) Zaher Mundher Yaseen et al.   ADVANCES IN ENGINEERING SOFTWARE

Multifunctional smart concretes with novel phase change materials: Mechanical and thermo-energy investigation

(2018) Antonella D'Alessandro et al.   APPLIED ENERGY

Compressive strength prediction of recycled concrete based on deep learning

(2018) Fangming Deng et al.   CONSTRUCTION AND BUILDING MATERIALS

PCM-mortar based construction materials for energy efficient buildings: A review on research trends

(2018) V. Venkateswara Rao et al.   ENERGY AND BUILDINGS

Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China

(2018) Junliang Fan et al.   ENERGY CONVERSION AND MANAGEMENT

Random forest regression prediction of solid particle Erosion in elbows

(2018) Peyman Zahedi et al.   POWDER TECHNOLOGY

Advanced machine learning techniques for building performance simulation: a comparative analysis

(2018) Debaditya Chakraborty et al.   Journal of Building Performance Simulation

A strategy to apply machine learning to small datasets in materials science

(2018) Ying Zhang et al.   npj Computational Materials

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

(2018) Palika Chopra et al.   Advances in Civil Engineering

Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods

(2018) Benjamin A. Young et al.   CEMENT AND CONCRETE RESEARCH

Use of random forests regression for predicting IRI of asphalt pavements

(2018) Hongren Gong et al.   CONSTRUCTION AND BUILDING MATERIALS

Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression

(2018) Muhammad Waseem Ahmad et al.   ENERGY

Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

(2018) Muhammad Waseem Ahmad et al.   JOURNAL OF CLEANER PRODUCTION

Random forest regression for online capacity estimation of lithium-ion batteries

(2018) Yi Li et al.   APPLIED ENERGY

Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions

(2018) Yung-Chia Chang et al.   APPLIED SOFT COMPUTING

Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach

(2018) Zaher Mundher Yaseen et al.   ENGINEERING STRUCTURES

Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model

(2018) Abeer A. Al-Musawi et al.   ENGINEERING WITH COMPUTERS

Effect of freeze-thaw cycles on the mechanical behavior of geopolymer concrete and Portland cement concrete containing micro-encapsulated phase change materials

(2018) Shima Pilehvar et al.   CONSTRUCTION AND BUILDING MATERIALS

Regression tree ensembles for wind energy and solar radiation prediction

(2017) Alberto Torres-Barrán et al.   NEUROCOMPUTING

Multi-site solar power forecasting using gradient boosted regression trees

(2017) Caroline Persson et al.   SOLAR ENERGY

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete

(2017) Faezehossadat Khademi et al.   Frontiers of Structural and Civil Engineering

Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests

(2016) Jun Ma et al.   APPLIED ENERGY

The influence of microencapsulated phase change material (PCM) characteristics on the microstructure and strength of cementitious composites: Experiments and finite element simulations

(2016) Matthew Aguayo et al.   CEMENT & CONCRETE COMPOSITES

Properties of cementitious mortar and concrete containing micro-encapsulated phase change materials

(2016) Amitha Jayalath et al.   CONSTRUCTION AND BUILDING MATERIALS

Early-age shrinkage and temperature optimization for cement paste by using PCM and MgO based on FBG sensing technique

(2016) Huafu Pei et al.   CONSTRUCTION AND BUILDING MATERIALS

Use of phase change materials (PCMs) to mitigate early age thermal cracking in concrete: Theoretical considerations

(2016) Branko Šavija et al.   CONSTRUCTION AND BUILDING MATERIALS

Mechanical and thermo-physical behaviour of concretes and mortars containing phase change material

(2015) T. Lecompte et al.   ENERGY AND BUILDINGS

Application of phase change materials to improve the thermal performance of cementitious material

(2015) Naser P. Sharifi et al.   ENERGY AND BUILDINGS

Phase change materials and thermal energy storage for buildings

(2015) Alvaro de Gracia et al.   ENERGY AND BUILDINGS

Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks

(2015) Vinay Chandwani et al.   EXPERT SYSTEMS WITH APPLICATIONS

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

(2015) V. Rodriguez-Galiano et al.   ORE GEOLOGY REVIEWS

On the feasibility of using phase change materials (PCMs) to mitigate thermal cracking in cementitious materials

(2014) Fabio Fernandes et al.   CEMENT & CONCRETE COMPOSITES

Machine learning in concrete strength simulations: Multi-nation data analytics

(2014) Jui-Sheng Chou et al.   CONSTRUCTION AND BUILDING MATERIALS

Encapsulated Phase-Change Materials as additives in cementitious materials to promote thermal comfort in concrete constructions

(2014) D. Snoeck et al.   MATERIALS AND STRUCTURES

Paraffin/diatomite composite phase change material incorporated cement-based composite for thermal energy storage

(2013) Biwan Xu et al.   APPLIED ENERGY

Use of phase change materials for thermal energy storage in concrete: An overview

(2013) Tung-Chai Ling et al.   CONSTRUCTION AND BUILDING MATERIALS

Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction

(2013) Halil Ibrahim Erdal   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Concrete compressive strength analysis using a combined classification and regression technique

(2012) Jui-Sheng Chou et al.   AUTOMATION IN CONSTRUCTION

Thermal, mechanical and microstructural analysis of concrete containing microencapsulated phase change materials

(2012) Pejman Keikhaei Dehdezi et al.   International Journal of Pavement Engineering

Gradient boosting trees for auto insurance loss cost modeling and prediction

(2011) Leo Guelman   EXPERT SYSTEMS WITH APPLICATIONS

Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques

(2010) Jui-Sheng Chou et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

The behavior of self-compacting concrete containing micro-encapsulated Phase Change Materials

(2009) M. Hunger et al.   CEMENT & CONCRETE COMPOSITES

Neural networks for predicting compressive strength of structural light weight concrete

(2009) Marai M. Alshihri et al.   CONSTRUCTION AND BUILDING MATERIALS

Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network

(2008) Cahit Bilim et al.   ADVANCES IN ENGINEERING SOFTWARE

Soft computing in engineering design – A review

(2007) K.M. Saridakis et al.   ADVANCED ENGINEERING INFORMATICS

Predicting the compressive strength of steel fiber added lightweight concrete using neural network

(2007) Fatih Altun et al.   COMPUTATIONAL MATERIALS SCIENCE

Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

(2007) İlker Bekir Topçu et al.   COMPUTATIONAL MATERIALS SCIENCE