A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Flexural Performance of Cross-Laminated Timber Panels Using Evolutionary Artificial Neural Networks
https://orcid.org/http://orcid.org/0000-0002-1765-1382
https://orcid.org/http://orcid.org/0000-0002-5045-6968
Cross-laminated timber (CLT) is a panelized engineering wood product known for its strong and lightweight properties. Construction with CLT panels has been a growing trend to meet the low-carbon building alternatives. This study generates a reliable artificial neural networks (ANNs)-based model for estimating the flexural performance of CLT panels. Genetic algorithm (GA) with multilayer perceptron (MLP) was implemented on a dataset of CLT panels considering width, span length, thickness, bending, and shearing strength variables as input parameters to determine the flexural strength of the panels. 70% of the data were used for training and 30% for testing phases. The accuracy of GA-based MLP model was evaluated by comparing the results with multiple linear regression (MLR) and a variety of feed-forward (FF) models. The results revealed that the GA-optimized MLP model could estimate the flexural strength of CLT panels with the highest accuracy.
Flexural Performance of Cross-Laminated Timber Panels Using Evolutionary Artificial Neural Networks
https://orcid.org/http://orcid.org/0000-0002-1765-1382
https://orcid.org/http://orcid.org/0000-0002-5045-6968
Cross-laminated timber (CLT) is a panelized engineering wood product known for its strong and lightweight properties. Construction with CLT panels has been a growing trend to meet the low-carbon building alternatives. This study generates a reliable artificial neural networks (ANNs)-based model for estimating the flexural performance of CLT panels. Genetic algorithm (GA) with multilayer perceptron (MLP) was implemented on a dataset of CLT panels considering width, span length, thickness, bending, and shearing strength variables as input parameters to determine the flexural strength of the panels. 70% of the data were used for training and 30% for testing phases. The accuracy of GA-based MLP model was evaluated by comparing the results with multiple linear regression (MLR) and a variety of feed-forward (FF) models. The results revealed that the GA-optimized MLP model could estimate the flexural strength of CLT panels with the highest accuracy.
Flexural Performance of Cross-Laminated Timber Panels Using Evolutionary Artificial Neural Networks
https://orcid.org/http://orcid.org/0000-0002-1765-1382
https://orcid.org/http://orcid.org/0000-0002-5045-6968
Cross-laminated timber (CLT) is a panelized engineering wood product known for its strong and lightweight properties. Construction with CLT panels has been a growing trend to meet the low-carbon building alternatives. This study generates a reliable artificial neural networks (ANNs)-based model for estimating the flexural performance of CLT panels. Genetic algorithm (GA) with multilayer perceptron (MLP) was implemented on a dataset of CLT panels considering width, span length, thickness, bending, and shearing strength variables as input parameters to determine the flexural strength of the panels. 70% of the data were used for training and 30% for testing phases. The accuracy of GA-based MLP model was evaluated by comparing the results with multiple linear regression (MLR) and a variety of feed-forward (FF) models. The results revealed that the GA-optimized MLP model could estimate the flexural strength of CLT panels with the highest accuracy.
Flexural Performance of Cross-Laminated Timber Panels Using Evolutionary Artificial Neural Networks
Lecture Notes in Civil Engineering
Desjardins, Serge (editor) / Poitras, Gérard J. (editor) / El Damatty, Ashraf (editor) / Elshaer, Ahmed (editor) / Malekabadi, Reza Abbasi (author) / Nikoo, Mehdi (author) / Hafeez, Ghazanfarah (author) / Bagchi, Ashutosh (author)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 11 ; Chapter: 26 ; 331-342
2024-09-26
12 pages
Article/Chapter (Book)
Electronic Resource
English
| DOAJ | 2024