Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

[1]  Pijush Samui,et al.  A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil , 2021, Engineering with Computers.

[2]  Pijush Samui,et al.  Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO , 2021 .

[3]  Panagiotis G. Asteris,et al.  Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models , 2020 .

[4]  Moncef L. Nehdi,et al.  Machine learning prediction of mechanical properties of concrete: Critical review , 2020 .

[5]  Liborio Cavaleri,et al.  Mapping and holistic design of natural hydraulic lime mortars , 2020 .

[6]  Jeffrey W. Bullard,et al.  RETRACTED: Experimental investigation and comparative machine-learning prediction of strength behavior of optimized recycled rubber concrete , 2020 .

[7]  Arul Arulrajah,et al.  Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes , 2020 .

[8]  Panagiotis G. Asteris,et al.  A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength , 2020, Neural Computing and Applications.

[9]  P. G. Asteris,et al.  Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models , 2020, Neural Computing and Applications.

[10]  Ali Behnood,et al.  Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm , 2020, Construction and Building Materials.

[11]  Liborio Cavaleri,et al.  On the metaheuristic models for the prediction of cement-metakaolin mortars compressive strength , 2020 .

[12]  Carmine Lima,et al.  Soft computing techniques in structural and earthquake engineering: a literature review , 2020 .

[13]  Panagiotis G. Asteris,et al.  A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model , 2020, Engineering with Computers.

[14]  P. G. Asteris,et al.  Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness , 2020, Sustainability.

[15]  Wang Yihong,et al.  Compressive strength of earth block masonry: Estimation based on neural networks and adaptive network-based fuzzy inference system , 2020 .

[16]  Vuong Minh Le,et al.  A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation , 2020, Sustainability.

[17]  G. Rutkowska,et al.  Modification of Ordinary Concrete Using Fly Ash from Combustion of Municipal Sewage Sludge , 2020, Materials.

[18]  Panagiotis G. Asteris,et al.  Concrete compressive strength using artificial neural networks , 2019, Neural Computing and Applications.

[19]  Panagiotis G. Asteris,et al.  Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength , 2019, Applied Sciences.

[20]  Panagiotis G. Asteris,et al.  A Gene Expression Programming Model for Predicting Tunnel Convergence , 2019, Applied Sciences.

[21]  Mohammadreza Koopialipoor,et al.  Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples , 2019, Engineering with Computers.

[22]  Panagiotis G. Asteris,et al.  Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate , 2019, Applied Sciences.

[23]  Danial Jahed Armaghani,et al.  A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets , 2019, Engineering with Computers.

[24]  Panagiotis G. Asteris,et al.  Prediction of the compressive strength of self-compactingconcrete using surrogate models , 2019 .

[25]  Panagiotis G. Asteris,et al.  Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns , 2019, Engineering with Computers.

[26]  P. Samui,et al.  Reliability-Based Slope Stability Analysis of Durgawati Earthen Dam Considering Steady and Transient State Seepage Conditions Using MARS and RVM , 2019, Indian Geotechnical Journal.

[27]  Hui Chen,et al.  Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models , 2019, Applied Sciences.

[28]  Amin Mohebkhah,et al.  Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects , 2019, Applied Sciences.

[29]  Liborio Cavaleri,et al.  Masonry Compressive Strength Prediction Using Artificial Neural Networks , 2018, TMM_CH.

[30]  Panagiotis G. Asteris,et al.  Soft computing-based techniques for concrete beams shear strength , 2019, Procedia Structural Integrity.

[31]  P. G. Asteris,et al.  Compressive strength of natural hydraulic lime mortars using soft computing techniques , 2019, Procedia Structural Integrity.

[32]  Danial Jahed Armaghani,et al.  Intelligent design of retaining wall structures under dynamic conditions , 2019 .

[33]  Pijush Samui,et al.  Reliability Analysis of Pile Foundation Using ELM and MARS , 2018, Geotechnical and Geological Engineering.

[34]  Panagiotis G. Asteris,et al.  Surface treatment of tool steels against galling failure , 2018 .

[35]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[36]  Fuzzy inference system to formulate compressive strength and ultimate strain of square concrete columns wrapped with fiber-reinforced polymer , 2016, Neural Computing and Applications.

[37]  Panagiotis G. Asteris,et al.  Prediction of self-compacting concrete strength using artificial neural networks , 2016 .

[38]  Nhat-Duc Hoang,et al.  Estimating Compressive Strength of High Performance Concrete with Gaussian Process Regression Model , 2016 .

[39]  Nhat-Duc Hoang,et al.  Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression , 2016, J. Comput. Civ. Eng..

[40]  Manoj Khandelwal,et al.  A new model based on gene expression programming to estimate air flow in a single rock joint , 2016, Environmental Earth Sciences.

[41]  Juan Luis Pérez-Ordóñez,et al.  Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming , 2016 .

[42]  Snehamoy Chatterjee,et al.  Ensemble Support Vector Machine Algorithm for Reliability Estimation of a Mining Machine , 2015, Qual. Reliab. Eng. Int..

[43]  Aminaton Marto,et al.  Neuro-fuzzy technique to predict air-overpressure induced by blasting , 2015, Arabian Journal of Geosciences.

[44]  O. Kisi,et al.  Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches , 2015 .

[45]  Danial Jahed Armaghani,et al.  Application of artificial neural network for predicting shaft and tip resistances of concrete piles , 2015 .

[46]  Dejan Vasovic,et al.  Prediction model for compressive strength of basic concrete mixture using artificial neural networks , 2014, Neural Computing and Applications.

[47]  Pijush Samui.  Determination of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel Using MPMR, MARS and LSSVM , 2014 .

[48]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[49]  Amir Hossein Gandomi,et al.  Intelligent Modeling and Prediction of Elastic Modulus of Concrete Strength via Gene Expression Programming , 2013, ICSI.

[50]  Guy Shani,et al.  Improving Simple Collaborative Filtering Models Using Ensemble Methods , 2013, MCS.

[51]  Pijush Samui,et al.  Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass , 2012, Geotechnical and Geological Engineering.

[52]  Pijush Samui,et al.  Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction , 2012, Soft Comput..

[53]  Juan R. Rabuñal,et al.  Optimization of existing equations using a new Genetic Programming algorithm: Application to the shear strength of reinforced concrete beams , 2012, Adv. Eng. Softw..

[54]  Amir Hossein Gandomi,et al.  A new predictive model for compressive strength of HPC using gene expression programming , 2012, Adv. Eng. Softw..

[55]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2011, Neural Computing and Applications.

[56]  J. Ideker,et al.  Advances in alternative cementitious binders , 2011 .

[57]  Rafat Siddique,et al.  Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks , 2011, Adv. Eng. Softw..

[58]  Hsing-Chih Tsai,et al.  Predicting high-strength concrete parameters using weighted genetic programming , 2011, Engineering with Computers.

[59]  Umit Atici,et al.  Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network , 2011, Expert Syst. Appl..

[60]  Jui-Sheng Chou,et al.  Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques , 2011, J. Comput. Civ. Eng..

[61]  K. Sathiyakumari,et al.  Prediction of the Compressive Strength of High Performance Concrete Mix using Tree Based Modeling , 2010 .

[62]  A. Chaipanich,et al.  Utilization of fly ash with silica fume and properties of Portland cement–fly ash–silica fume concrete , 2010 .

[63]  Okan Karahan,et al.  Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete , 2009, Adv. Eng. Softw..

[64]  Mohammed Sonebi,et al.  Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash , 2009 .

[65]  Mustafa Saridemir,et al.  Prediction of compressive strength of concretes containing metakaolin and silica fume by artificial neural networks , 2009, Adv. Eng. Softw..

[66]  B. V. Venkatarama Reddy,et al.  Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN , 2009 .

[67]  G. Trtnik,et al.  Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. , 2009, Ultrasonics.

[68]  İlker Bekir Topçu,et al.  Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic , 2008 .

[69]  Mohammad Hossein Fazel Zarandi,et al.  Fuzzy polynomial neural networks for approximation of the compressive strength of concrete , 2008, Appl. Soft Comput..

[70]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[71]  I-Cheng Yeh,et al.  Analysis of Strength of Concrete Using Design of Experiments and Neural Networks , 2006 .

[72]  Manish A. Kewalramani,et al.  Prediction of Concrete Strength Using Neural-Expert System , 2006 .

[73]  Türkay Dereli,et al.  Prediction of cement strength using soft computing techniques , 2004 .

[74]  Carlos Videla,et al.  Modeling Portland Blast-Furnace Slag Cement High-Performance Concrete , 2004 .

[75]  E. Gartner.  Industrially interesting approaches to “low-CO2” cements ☆ , 2004 .

[76]  Gokmen Tayfur,et al.  FUZZY LOGIC MODEL FOR THE PREDICTION OF CEMENT COMPRESSIVE STRENGTH , 2004 .

[77]  Antoni Cladera,et al.  Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups , 2004 .

[78]  Murat Dicleli,et al.  Predicting the shear strength of reinforced concrete beams using artificial neural networks , 2004 .

[79]  Rafat Siddique,et al.  Performance characteristics of high-volume Class F fly ash concrete , 2004 .

[80]  Joong Hoon Kim,et al.  Genetic algorithm in mix proportioning of high-performance concrete , 2004 .

[81]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[82]  Gregory Z. Grudic,et al.  A Formulation for Minimax Probability Machine Regression , 2002, NIPS.

[83]  Wps Dias,et al.  NEURAL NETWORKS FOR PREDICTING PROPERTIES OF CONCRETES WITH ADMIXTURES , 2001 .

[84]  Michael I. Jordan,et al.  Minimax Probability Machine , 2001, NIPS.

[85]  Thomas G. Dietterich.  Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[86]  I-Cheng Yeh,et al.  Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming , 1999 .

[87]  I-Cheng Yeh,et al.  Modeling of strength of high-performance concrete using artificial neural networks , 1998 .

[88]  Chi Sun Poon,et al.  Effect of Fly Ash and Silica Fume on Compressive and Fracture Behaviors of Concrete , 1998 .

[89]  Leo Breiman,et al.  Bias, Variance , And Arcing Classifiers , 1996 .

[90]  K. Ganesh Babu,et al.  Efficiency of silica fume in concrete , 1995 .

[91]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .