Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models.

[1]  K. Ganesh Babu,et al.  Early strength behaviour of fly ash concretes , 1994 .

[2]  B. B. Sabir,et al.  Mechanical properties and frost resistance of silica fume concrete , 1997 .

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

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

[5]  J. J. Brooks,et al.  Effect of silica fume on mechanical properties of high-strength concrete , 2004 .

[6]  Norbert J. Delatte,et al.  Effect of supplementary cementitious materials on the compressive strength and durability of short-term cured concrete , 2004 .

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

[8]  B. Sengupta,et al.  Influence of Silica Fume on the Tensile Strength of Concrete , 2005 .

[9]  C. Atiş.  Strength Properties of High-Volume Fly Ash Roller Compacted and Workable Concrete, and Influence of Curing Condition , 2005 .

[10]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[11]  Aminaton Marto,et al.  Simulation of blasting-induced air overpressure by means of artificial neural networks , 2012 .

[12]  Xiaoping Zhou,et al.  Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading , 2014 .

[13]  Meldi Suhatril,et al.  Fuzzy modelling approach for shear strength prediction of RC deep beams , 2015 .

[14]  Danial Jahed Armaghani,et al.  Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods , 2015, Engineering with Computers.

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

[16]  V. J. Majd,et al.  Application of fuzzy inference system for prediction of rock fragmentation induced by blasting , 2015, Arabian Journal of Geosciences.

[17]  Haiqing Yang,et al.  A quasi-three-dimensional spring-deformable-block model for runout analysis of rapid landslide motion , 2015 .

[18]  Tong Wang,et al.  A four-node membrane element model with bending modification for one-step algorithm for bus rollover impact , 2015 .

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

[20]  Danial Jahed Armaghani,et al.  Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances , 2015, Engineering with Computers.

[21]  Hani S. Mitri,et al.  Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction , 2015, Natural Hazards.

[22]  Aminaton Marto,et al.  Prediction of blast-induced air overpressure: a hybrid AI-based predictive model , 2015, Environmental Monitoring and Assessment.

[23]  Danial Jahed Armaghani,et al.  Rock strength estimation: a PSO-based BP approach , 2016, Neural Computing and Applications.

[24]  Xiaoping Zhou,et al.  Analysis on the damage behavior of mixed ground during TBM cutting process , 2016 .

[25]  Jian Zhou,et al.  Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining , 2016 .

[26]  Mohammad Javad Rahimdel,et al.  Haulage system selection for open pit mines using fuzzy MCDM and the view on energy saving , 2016, Neural Computing and Applications.

[27]  Bhatawdekar Ramesh Murlidhar,et al.  Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique , 2016, Environmental Earth Sciences.

[28]  Raheb Bagherpour,et al.  A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting , 2016, Engineering with Computers.

[29]  Masoud Monjezi,et al.  An optimized ANN model based on genetic algorithm for predicting ripping production , 2017, Neural Computing and Applications.

[30]  Liborio Cavaleri,et al.  Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks , 2015, Comput. Intell. Neurosci..

[31]  Raheb Bagherpour,et al.  Forecasting ground vibration due to rock blasting: a hybrid intelligent approach using support vector regression and fuzzy C-means clustering , 2018, Engineering with Computers.

[32]  Danial Jahed Armaghani,et al.  A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels , 2019, Bulletin of Engineering Geology and the Environment.

[33]  Manoj Khandelwal,et al.  Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples , 2018, Engineering with Computers.

[34]  Yun Zhang,et al.  Efficient numerical simulation of injection mold filling with the lattice Boltzmann method , 2017 .

[35]  Panagiotis G. Asteris,et al.  Self-compacting concrete strength prediction using surrogate models , 2017, Neural Computing and Applications.

[36]  Hani S. Mitri,et al.  Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel , 2017 .

[37]  Aminaton Marto,et al.  Predicting tunnel boring machine performance through a new model based on the group method of data handling , 2018, Bulletin of Engineering Geology and the Environment.

[38]  M. Nilashi,et al.  Rock tensile strength prediction using empirical and soft computing approaches , 2018, Bulletin of Engineering Geology and the Environment.

[39]  Danial Jahed Armaghani,et al.  Three hybrid intelligent models in estimating flyrock distance resulting from blasting , 2018, Engineering with Computers.

[40]  Edy Tonnizam Mohamad,et al.  Overbreak prediction and optimization in tunnel using neural network and bee colony techniques , 2018, Engineering with Computers.

[41]  Edy Tonnizam Mohamad,et al.  Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques , 2018, Engineering with Computers.

[42]  Haiqing Yang,et al.  Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass , 2018, Tunnelling and Underground Space Technology.

[43]  Karzan Wakil,et al.  Application of support vector machine with firefly algorithm for investigation of the factors affecting the shear strength of angle shear connectors , 2018 .

[44]  Jian Zhou,et al.  Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects , 2018 .

[45]  Mohammadreza Koopialipoor,et al.  A Risk-Based Technique to Analyze Flyrock Results Through Rock Engineering System , 2018, Geotechnical and Geological Engineering.

[46]  Haiqing Yang,et al.  Investigation on the Cracking Character of Jointed Rock Mass Beneath TBM Disc Cutter , 2018, Rock Mechanics and Rock Engineering.

[47]  Jian Zhou,et al.  Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance , 2019, Natural Resources Research.

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

[49]  Jian Zhou,et al.  A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network , 2019, Engineering with Computers.

[50]  Liborio Cavaleri,et al.  Krill herd algorithm-based neural network in structural seismic reliability evaluation , 2019 .

[51]  Liborio Cavaleri,et al.  Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks , 2019, Applied Sciences.

[52]  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.

[53]  Mohammadreza Koopialipoor,et al.  A new approach for estimation of rock brittleness based on non-destructive tests , 2019, Nondestructive Testing and Evaluation.

[54]  Annan Zhou,et al.  Perspectives for flood risk assessment and management for mega-city metro system , 2019, Tunnelling and Underground Space Technology.

[55]  Danial Jahed Armaghani,et al.  The use of new intelligent techniques in designing retaining walls , 2019, Engineering with Computers.

[56]  Panagiotis G. Asteris,et al.  Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures , 2019, Neural Computing and Applications.

[57]  Bhatawdekar Ramesh Murlidhar,et al.  A new hybrid method for predicting ripping production in different weathering zones through in situ tests , 2019 .

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

[59]  Mahdi Hasanipanah,et al.  Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm , 2020, Engineering with Computers.

[60]  Danial Jahed Armaghani,et al.  The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production , 2020, Engineering with Computers.

[61]  Danial Jahed Armaghani,et al.  Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions , 2019, Soft Comput..

[62]  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.

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

[64]  Danial Jahed Armaghani,et al.  Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance , 2019, Engineering with Computers.

[65]  Edy Tonnizam Mohamad,et al.  A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls , 2018, Eng. Comput..

[66]  A. Marto,et al.  Application of several optimization techniques for estimating TBM advance rate in granitic rocks , 2019, Journal of Rock Mechanics and Geotechnical Engineering.

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

[68]  Aydin Azizi,et al.  A new methodology for optimization and prediction of rate of penetration during drilling operations , 2019, Engineering with Computers.

[69]  Manoj Khandelwal,et al.  Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques , 2019, Engineering with Computers.

[70]  Danial Jahed Armaghani,et al.  Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique , 2019, IEEE Access.

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

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

[73]  Ebrahim Noroozi Ghaleini,et al.  Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN , 2019, Environmental Earth Sciences.

[74]  Ahmadreza Hedayat,et al.  Application of deep neural networks in predicting the penetration rate of tunnel boring machines , 2019, Bulletin of Engineering Geology and the Environment.

[75]  Jian Zhou,et al.  Deep neural network and whale optimization algorithm to assess flyrock induced by blasting , 2021, Engineering with Computers.