Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling

Eidgahee, Danial Rezazadeh; Haddad, Abdolhosein; Naderpour, Hosein

doi:10.24200/sci.2018.5663.1408

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…In fact, the network is a massive parallel system consisting of several elements processed by weighted links. Feed Forward and Feed Forward Back Propagation (FFBP) networks are the most popular ANNs [20].…”

Section: Overview Of Artificial Neural Networkmentioning

confidence: 99%

Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

Kaveh

Khavaninzadeh

2023

Period. Polytech. Civil Eng.

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In recent years, artificial neural network (ANN) has become one of the popular and effective machine learning models, having a unique ability to handle very complex problems and the potential to predict accurate results without a defined algorithmic solution. However, the ANN structure and parameters are usually chosen by experience.The behavior of Partially Grouted (PG) masonry shear walls is complex due to the inherent anisotropic properties of the masonry materials and the nonlinear interactions between mortar, blocks, grouted cells, non-grouted cells, and reinforcing steel.In this study, the aim is to develop an artificial neural network model by combining the ECBO meta-heuristic algorithm with the artificial neural network structure to optimize the feed forward propagation network parameters for analyzing the shear strength of PG walls.A total of 255 test data on PG collected from the available literature were used to generate training and test data sets. Various validation criteria such as mean square error, root mean square error and correlation coefficient (R) are used to validate the models.In this study, the optimal number of neurons used in the hidden layer and also the optimal number of CBs required in the ECBO algorithm were obtained. The mathematical formulation of the optimized neural network model with the combination of meta-heuristic algorithm is also presented.

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Section: Overview Of Artificial Neural Networkmentioning

confidence: 99%

Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

Kaveh

Khavaninzadeh

2023

Period. Polytech. Civil Eng.

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“…The application of an ANN for the prediction of the specific gravity and compaction properties of highly heterogeneous material and fly ash was performed by Das and Sabat [88]. The shear strength parameters of granulated waste rubber were determined by Eidgahee et al [89] using an ANN and the group method of data handling. An ANN model using sieve analysis, Atterberg limits, optimum moisture content, and maximum dry density data was applied by Bhatt and Jain [90] to predict the California bearing ratio of soils.…”

Section: Materials Testing and Controlmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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“…MATLAB was used to train the connecting weights of network neurons using feedforward backpropagation and the Levenberg-Marquardt method [94]. The literature has supported the usage of one hidden layer to tackle various nonlinear problems [85,95]. The optimum artificial neural network was created using the trial-and-error approach.…”

Section: Proposed Ann Modelsmentioning

confidence: 99%

Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

et al. 2021

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Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency.

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Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling

Cited by 17 publications

References 37 publications

Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

Artificial Neural Networks for Sustainable Development of the Construction Industry

Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques

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