Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm

Kandiri, Amirreza; Golafshani, Emadaldin Mohammadi; Behnood, Ali

doi:10.1016/j.conbuildmat.2020.118676

Cited by 143 publications

(72 citation statements)

References 63 publications

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“…These innovative teaching methods, with the aim of strengthening the teaching offer and learning processes, are aimed at a greater understanding of the individual learning processes of the group dynamics that are triggered in a classroom. Specially with the increasing number of digital technologies in the construction industry such as use of AI to predict compressive strength of concrete [15][16][17][18], and other environmental automated systems [19][20][21][22][23][24][25][26][27][28][29], the need for VR integration becomes more obvious than ever.…”

Section: Resultsmentioning

confidence: 99%

A methodology to virtualize technical engineering laboratories: MastrLAB-VR

Scidà¹,

Alotto²,

Osello³

2021

JCM

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Due to the importance given today to innovation, the education sector is evolving thanks digital technologies. Virtual Reality (VR) can be a potential teaching tool offering many advantages in the field of training and education, as it allows to acquire theoretical knowledge and practical skills using an immersive experience in less time than the traditional educational process. These assumptions allow to lay the foundations for a new educational environment, involving and stimulating for students. Starting from the objective of strengthening the innovative teaching offer and the learning processes, the case study of the research concerns the digitalization of MastrLAB, High Quality Laboratory (HQL) belonging to the Department of Structural, Building and Geotechnical Engineering (DISEG) of the Polytechnic of Turin, a center specialized in experimental mechanical tests on traditional and innovative building materials and on the structures made with them. The MastrLAB-VR has been developed, a revolutionary innovative training tool designed with the aim of educating the class in total safety on the techniques of use of machinery, thus reducing the dangers arising from the performance of potentially dangerous activities. The virtual laboratory, dedicated to the students of the Building and Civil Engineering Courses of the Polytechnic of Turin, has been projected to simulate in an absolutely realistic way the experimental approach to the structural tests foreseen in their courses of study: from the tensile tests to the relaxation tests, from the steel qualification tests to the resilience tests on elements at environmental conditions or at characterizing temperatures. The research work proposes a methodology for the virtualization of technical laboratories through the application of Building Information Modelling (BIM), starting from the creation of a digital model. The process includes the creation of an independent application, which with Oculus Rift technology will allow the user to explore the environment and interact with objects through the use of joypads. The application has been tested in prototype way on volunteers, obtaining results related to the acquisition of the educational notions exposed in the experience through a virtual quiz with multiple answers, achieving an overall evaluation report. The results have shown that MastrLAB-VR is suitable for both beginners and experts and will be adopted experimentally for other laboratories of the University departments.

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Section: Resultsmentioning

confidence: 99%

A methodology to virtualize technical engineering laboratories: MastrLAB-VR

Scidà¹,

Alotto²,

Osello³

2021

JCM

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“…Therefore, for generating ANN models that have different complexities and accuracies, developing a model with this ability is seriously needed. In this study, to develop a multi-objective artificial neural network (MOANN) [10], salp swarm algorithm that is a multi-objective optimization algorithm is combined with an ANN [45]. In Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The models, which have been developed recently, have a better performance than old one like genetic algorithm or particle swarm optimization algorithm. For instance, in a study multi-objective salp swarm algorithm hybridized with ANN to predict compressive strength of concrete with GGBFS [10]. In another study multi-objective grey wolf optimization algorithm and an ANN used to predict compressive strength of concrete containing silica fume [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the module of elasticity of green concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and Salp swarm algorithm

Kandiri¹,

Fotouhi²

2021

JCM

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Ground granulated blast furnave slag (GGBFS) once used in concrete derives both technical and economic advantages. Energy consumption and greenhouse gas emissions can be reduced significantly if cement is replaced by GGBFS in concrete mixtures. However, it is necessary to develop a detailed model in order to evaluate the elastic modulus of the concretes containing GGBFS because of important role of its value as a parameter in different design codes. In addition, it can save energy, cost, and time in comparison to direct laboratory-based measurements. In this research, to develop a model for the estimation of the elastic modulus of concretes containing GGBFS, Artificial neural network (ANN) was used. A multi-objective optimization method titled multi-objective slap swarm algorithm (MOSSA) was proposed to optimize the error and complexity of the developed ANN models. To develop predictive models of elastic modulus. Besides, one of the most used classification techniques to solve engineering problems that is The M5P model tree algorithm was used in order to develop predictive models of elastic modulus. The efficiency of the proposed model developed based on the ANN algorithm was compared with that of the model developed based on the M5P model tree technique with the help of several error measures. The result of this research is that it is possible using the M5P model tree and the proposed ANN model for the purpose of provide predictive tools for estimating the elastic modulus of concretes containing GGBFS and these would have 13.36% and 1.79% mean absolute percentage error (MAPE), respectively. It is understood from these values that the proposed model based on ANN algorithm is much more effective than the one developed using M5P model tree.

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“…Concretes containing GGBFS CS ANN with MOSSA, M5P [11] Concrete containing waste foundry sand CS, FS, EM, ST M5P [12] Normal and high-strength concretes with fly ash and/or GGBFS CS M5P [13], Fuzzy logic [14], ANN [6,[14][15][16][17] High-performance concrete made with copper slag and nanosilica…”

Section: Concrete Type Property Ai Modelmentioning

confidence: 99%

Prediction of the creep coefficient of green concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and Salp swarm algorithm

Kandiri¹,

Fotouhi²

2021

JCM

Self Cite

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Using ground granulated blast furnace slag (GGBFS) in concrete mixtures that is known as supplementary materials, has both technical and economic advantages. The using of energy and greenhouse gas emission can be less if cement replaced by GGBFS in concrete mixtures. It is necessary to develop a detailed model in order to evaluate the creep coefficient of the concretes containing GGBFS because of important role of its value as a parameter in different design codes. In addition, it can save energy, cost, and time in comparison to direct laboratory-based measurements. In this research, to develop a model for the estimation of the creep coefficient of concretes containing GGBFS, Artificial neural network (ANN) was used. A multi-objective optimization method titled multi-objective slap swarm algorithm (MOSSA) was proposed to optimize the error and complexity of the developed ANN models. To develop predictive models of creep coefficient. Besides, one of the most used classification techniques to solve engineering problems which is The M5P model tree algorithm was used in order to develop predictive models of creep coefficient. The efficiency of the proposed model developed based on the ANN algorithm was compared with that of the model developed based on the M5P model tree technique with the help of several error measures. The findings from this research indicate that the M5P model tree and the proposed ANN model can successfully provide predictive tools for estimating the creep coefficient of concretes containing GGBFS with 13.36% and 1.79% mean absolute percentage error (MAPE), respectively. These values indicate that the proposed model based on ANN algorithm has superior efficiency compared to the one developed using M5P model tree.

show abstract

Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm

Cited by 143 publications

References 63 publications

A methodology to virtualize technical engineering laboratories: MastrLAB-VR

A methodology to virtualize technical engineering laboratories: MastrLAB-VR

Prediction of the module of elasticity of green concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and Salp swarm algorithm

Prediction of the creep coefficient of green concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and Salp swarm algorithm

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