A comparative investigation using machine learning methods for concrete compressive strength estimation

A fuzzy weighted relative error support vector machine for reverse prediction of concrete components

(2020) Zongwen Fan et al.   COMPUTERS & STRUCTURES

Machine learning study of the mechanical properties of concretes containing waste foundry sand

(2020) Ali Behnood et al.   CONSTRUCTION AND BUILDING MATERIALS

Depth detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion and decision tree

(2020) Dongdong Chen et al.   MEASUREMENT

Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms

(2020) Fatih Tufaner et al.   ENVIRONMENTAL MONITORING AND ASSESSMENT

Designing sustainable concrete mixture by developing a new machine learning technique

(2020) Hamed Naseri et al.   JOURNAL OF CLEANER PRODUCTION

Predicting uniaxial compressive strength of oil palm shell concrete using a hybrid artificial intelligence model

(2020) Junfei Zhang et al.   Journal of Building Engineering

Machine learning-based models for the concrete breakout capacity prediction of single anchors in shear

(2020) Oladimeji B. Olalusi et al.   ADVANCES IN ENGINEERING SOFTWARE

Experimental investigation and comparative machine-learning prediction of strength behavior of optimized recycled rubber concrete

(2020) Mostafa Jalal et al.   CONSTRUCTION AND BUILDING MATERIALS

Revisiting the concept of characteristic compressive strength of concrete

(2020) Chi-Cong Vu et al.   CONSTRUCTION AND BUILDING MATERIALS

Reduce graphene oxide-manganese oxide-black cumin based hybrid composite (rGO-MnO2/BC): A novel material for water remediation

(2020) Nusrat Tara et al.   Materials Today Communications

Prediction of plastic yield surface for porous materials by a machine learning approach

(2020) W.Q. Shen et al.   Materials Today Communications

Effect of concrete mixture components, proportioning, and compressive strength on fracture parameters

(2019) Aref Shafiei Dastgerdi et al.   CONSTRUCTION AND BUILDING MATERIALS

Machine Learning Techniques in Concrete Mix Design

(2019) Patryk Ziolkowski et al.   Materials

A novel study for the estimation of crack propagation in concrete using machine learning algorithms

(2019) Gökhan Bayar et al.   CONSTRUCTION AND BUILDING MATERIALS

Estimation of the degree of hydration of concrete through automated machine learning based microstructure analysis – A study on effect of image magnification

(2019) Srikanth Sagar Bangaru et al.   ADVANCED ENGINEERING INFORMATICS

Monitor concrete moisture level using percussion and machine learning

(2019) Liqiong Zheng et al.   CONSTRUCTION AND BUILDING MATERIALS

Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach

(2019) De-Cheng Feng et al.   CONSTRUCTION AND BUILDING MATERIALS

Computational design optimization of concrete mixtures: A review

(2018) M.A. DeRousseau et al.   CEMENT AND CONCRETE RESEARCH

Concrete material science: Past, present, and future innovations

(2018) Henri Van Damme   CEMENT AND CONCRETE RESEARCH

An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack

(2018) Huaicheng Chen et al.   PLoS One

Support vector machine based reliability analysis of concrete dams

(2018) Mohammad Amin Hariri-Ardebili et al.   SOIL DYNAMICS AND EARTHQUAKE 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

Non-destructive assessment of both mean strength and variability of concrete: A new bi-objective approach

(2016) Maitham Alwash et al.   CONSTRUCTION AND BUILDING MATERIALS

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

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

Damage prediction for regular reinforced concrete buildings using the decision tree algorithm

(2013) A. Karbassi et al.   COMPUTERS & STRUCTURES

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

(2013) Halil Ibrahim Erdal   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

High-performance Concrete Compressive Strength Prediction using Time-Weighted Evolutionary Fuzzy Support Vector Machines Inference Model

(2012) Min-Yuan Cheng et al.   AUTOMATION IN CONSTRUCTION

ANN modeling of the bremsstrahlung photon flux in tantalum target

(2012) Kadir Gunoglu et al.   NEURAL COMPUTING & APPLICATIONS

Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm

(2011) Chengjun Zhan et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic

(2008) Mustafa Sarıdemir et al.   CONSTRUCTION AND BUILDING MATERIALS