Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm

Yan, Fei; Lin, Zhibin; Wang, Xingyu; Azarmi, Fardad; Соболев, Константин

doi:10.1016/j.compstruct.2016.11.068

Cited by 118 publications

(42 citation statements)

References 27 publications

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“…ANNs have been used successfully in civil engineering applications. Yan et al [44] predicted the concrete bond strength of a glass fiber-reinforced polymer bar using an ANN optimized via a GA. Najjar and Basheer [45] predicted soil characteristics and uncertainty using an ANN. Shahin et al [46] used an ANN to predict the settlement of shallow foundations.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading

et al. 2020

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Bridge displacements are one of the most important physical values in evaluating the health of bridges. However, the direct measurement of bridge displacements is not easy due to various factors, such as installation location and cost. For that reason, in a previous study (part 1), a method for predicting bridge displacements from strains was proposed using an artificial neural network (ANN), which has a strong ability in data mapping. In this paper, to predict the overall displacements from a small number of strains more efficiently, a method to optimize the number and locations of strain-measurement points was proposed using the genetic algorithm (GA), which is widely used for global optimization. To verify the proposed methods, two cases, a simple beam under sinusoidal loads and a girder bridge under vehicle loads, are carried out through numerical analysis. Also, a laboratory experiment is carried out with a vibrating cantilever beam. The results indicate that the predicted displacements from at least two strains at the optimized locations show good agreements with displacements by numerical analysis and measurements. The results suggest that the proposed method (optimization of strain-measurement points) is very efficient and can be applied in the actual field.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading

et al. 2020

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“…Recent applications of machine learning and neural networks in structural engineering are mostly related to prediction and modelling of elastic properties of materials [inter alia 16,17], compressive and bond strength of concrete [e.g. 18,19], buckling load [20][21][22], development of cementitious composites [23], and the refinement finite element models [e.g. 24].…”

Section: Machine and Deep Learning In Structural And Civil Engineeringmentioning

confidence: 99%

Machine Learning for Sustainable Structures: A Call for Data

et al. 2019

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Buildings are the world's largest contributors to energy demand, greenhouse gases (GHG) emissions, resource consumption and waste generation. An unmissable opportunity exists to tackle climate change, global warming, and resource scarcity by rethinking how we approach building design. Structural materials often dominate the total mass of a building; therefore, a significant potential for material efficiency and GHG emissions mitigation is to be found in efficient structural design and use of structural materials. To this end, environmental impact assessment methods, such as life cycle assessment (LCA), are increasingly used. However, they risk failing to deliver the expected benefits due to the high number of parameters and uncertainty factors that characterise impacts of buildings along their lifespans. Additionally, effort and cost required for a reliable assessment seem to be major barriers to a more widespread adoption of LCA. More rapid progress towards reducing building impacts seems therefore possible by combining established environmental impact assessment methods with artificial intelligence approaches such as machine learning and neural networks. This short communication will briefly present previous attempts to employ such techniques in civil and structural engineering. It will present likely outcomes of machine learning and neural network applications in the field of structural engineering andmost importantly-it calls for data from professionals across the globe to form a fundamental basis which will enable quicker transition to a more sustainable built environment.

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“…Ahmed and Nehdi (2017) presented an approach to predicting the intrinsic self-healing in concrete using a hybrid GAartificial NN. Yan et al (2017Yan et al ( ,2016 combined the strong nonlinear mapping ability of ANN with the global searching ability of GA to study the diameter, surface, position, and embedment length of the steel, as well as the thickness of the concrete cover and concrete compressive strength on the influence of the glass fiber reinforced plastic (GFRP) bond strength of reinforcement and concrete, and they studied the anchorage reliability of GFRP steel given the factors of steel diameter, thickness of concrete cover, anchoring length, concrete compressive strength and ultimate yield strength of GFRP steel. However, few people use GA-BP NN to study the prediction of the flexural capacity of RC beams after fire controlled by multiple factors.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning

Cai

Gao

2020

J. Perform. Constr. Facil.

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To accurately predict the flexural capacity of post-fire RC beams is imperative for fire safety design. In this paper, the residual flexural capacity of post-fire RC beams is predicted based on a back-propagation (BP) neural network (NN) optimized by a genetic algorithm (GA). First, the temperature distribution of the beams was determined using the finite element analysis software ABAQUS, and the strength reduction factor of materials was determined. The flexural capacity of the RC beams after fire is calculated by the flexural strength reduction calculation model. The model is used to generate the training data for the NN. To enable machine learning, 480 datasets are produced, of which 360 datasets are used to train the network; the remaining 120 datasets are used to test the network. The predictive models are constructed using BPNN and GA-BPNN respectively. The prediction accuracy is evaluated by comparing the predicted values and the target values. The comparison shows that the GA-BPNN has a faster convergence speed, higher stability, and can reach the goal more times, reducing the possibility of BPNN falling into the local optimum and achieving the global optimum. The proposed GA-BPNN model for predicting the flexural capacity of post-fire RC beams provides a new approach for design practice.

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Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm

Cited by 118 publications

References 27 publications

Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading

Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 2): Optimization of Strain-Measurement Points by a Genetic Algorithm under Dynamic Loading

Machine Learning for Sustainable Structures: A Call for Data

Prediction of the Postfire Flexural Capacity of RC Beam Using GA-BPNN Machine Learning

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