Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

Mashhadban, Hadi; Kutanaei, Saman Soleimani; Sayarinejad, M. A.

doi:10.1016/j.conbuildmat.2016.05.034

Cited by 156 publications

(53 citation statements)

References 24 publications

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“…Artificial neural network (ANN) was employed for modeling the nonrelationships between inputs (influencing variables) and outputs (eg, UCS, Young modulus) in building and construction materials. 38,39 In a neural network, the neurons are grouped in each layer. Typically, a neuron has various inputs and a single output.…”

Section: Bpnnmentioning

confidence: 99%

Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes

Sun

Zhang

et al. 2019

Num Anal Meth Geomechanics

141

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Summary This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction.

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Section: Bpnnmentioning

confidence: 99%

Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes

Sun

Zhang

et al. 2019

Num Anal Meth Geomechanics

141

View full text Add to dashboard Cite

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“…Efe Camci et al proposed a novel particle swarm optimization sliding mode control theory‐based hybrid algorithm for the training of the type‐2 fuzzy neural network, and the effectiveness of the proposed method was verified based on simulation analysis . Hadi Mashhadban used the experimental data of self‐compacting concrete to train the feed forward artificial neural network and combined the artificial neural network and particle swarm optimization algorithm to predict the properties of self‐compacting concrete; simulation results showed that the particle swarm algorithm with the artificial neural network is a flexible and accurate method …”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Zhao

Ren

Gao

et al. 2019

Quality & Reliability Eng

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The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

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“…For instance, in a feed-forward network, nodes are structured in parallel multilayers that can be classified into input layer, hidden layers, and output layer [39,40]. …”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

Suleiman

Nehdi

2017

Materials

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This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm-artificial neural network (GA-ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA-ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.

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Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

Cited by 156 publications

References 24 publications

Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes

Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

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