A Deep Learning Approach to Automated Structural Engineering of Prestressed Members

Torky, Ahmed A.; Aburawwash, Anas A.

doi:10.18178/ijscer.7.4.347-352

Cited by 9 publications

(3 citation statements)

References 2 publications

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“…By using a deep learning approach, automated structural analysis and design of prestressed members can be achieved. For example, deep learning and grid search accessible hyperparameters can be used to anticipate optimum prestressing of members without requiring structural engineers to perform endless analysis and design iterations [105]. Yoo et al [106] has actively conducted a conceptual computer-aided engineering (CAE) to demonstrate the future possibilities of structural engineering.…”

Section: Structural Analysis and Designmentioning

confidence: 99%

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Baduge

Thilakarathna

Perera

et al. 2022

Automation in Construction

432

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Section: Structural Analysis and Designmentioning

confidence: 99%

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Baduge

Thilakarathna

Perera

et al. 2022

Automation in Construction

432

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“…Principal component analysis and radial basis function neural networks are employed to predict concrete compressive strength, achieving high accuracy and meeting engineering test accuracy requirements [16]. Deep learning techniques, such as neural networks, offer efficient design solutions for prestressed concrete members, improving structural engineering practices [17]. Adaptive Neuro-Fuzzy Inference System (ANFIS) predicts compressive strength of self-compacting concrete containing treated palm oil fuel ash, demonstrating high accuracy and efficiency over experimental methods [18].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Compressive Strength of Concrete by using Machine Learning Techniques

Bandaru,

Kyle,

Priyadarshini

et al. 2024

J. Phys.: Conf. Ser.

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Considerable efforts have been made to increase the strength of concrete by substituting some of the cement in the concrete with industrial waste, such as fly ash. Predicting the compressive strength of concrete, however, is a difficult undertaking since it depends on a number of elements, including the water-to-cement ratio and the size and form of the particles. The work on machine learning algorithms for Evaluating the strength of concrete with the inclusion of fly ash is presented in this publication. In order to determine the most accurate estimation technique, the study also compares the accuracy of various machine learning models, including ensemble models and non-ensemble models in predicting CS. For this purpose, a dataset with a diverse set of experimental findings, encompassing a broad spectrum of compressive strength values, was gathered from established literature and verified through statistical scrutiny. The amounts of cement, fine aggregate, coarse aggregates, fly ash, water content, percentage of superplasticizer, and curing days were the main input parameters for the ML models, while the output was the CS of concrete. The assessment of performance involved the utilization of various performance metrics, including MSE & R2 to assess accuracy and reliability. The comparison reveals that XGBoost Regressor, Bagging Regressor, and Random Forest are the most reliable models. Fly ash and other parameters’ effects on CS prediction were also fully understood thanks to the use of sensitivity and parametric analysis, which also helped to shed light on the relationship between input parameters and CS. The efficiency of machine learning methods is demonstrated in the study. It eliminates the need for expensive and time-consuming experimental experiments by providing researchers with a quicker and more economical way to assess how fly ash and other factors affect CS estimation.

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“…[4,5]), deep learning and neural networks (e.g. [6][7][8][9]), constraint search (e.g. [10]), evolutionary computation and genetic algorithm (e.g.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

Monte Carlo Tree Search as an intelligent search tool in structural design problems

Rossi

Winands

Butenweg

2021

Engineering with Computers

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Monte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study’s outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers.

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A Deep Learning Approach to Automated Structural Engineering of Prestressed Members

Cited by 9 publications

References 2 publications

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Predicting the Compressive Strength of Concrete by using Machine Learning Techniques

Monte Carlo Tree Search as an intelligent search tool in structural design problems

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