Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings
Wind-structures interaction has been extensively examined in the last few decades using field measurements, full scale measurements and wind tunnel testing. These experimental approaches are considered costly and time consuming. The need for a reliable analytical approach that can be used for examin...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682401202X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832595916474810368 |
|---|---|
| author | Mosbeh R. Kaloop Abidhan Bardhan Pijush Samui Jong Wan Hu Mohamed Elsharawy |
| author_facet | Mosbeh R. Kaloop Abidhan Bardhan Pijush Samui Jong Wan Hu Mohamed Elsharawy |
| author_sort | Mosbeh R. Kaloop |
| collection | DOAJ |
| description | Wind-structures interaction has been extensively examined in the last few decades using field measurements, full scale measurements and wind tunnel testing. These experimental approaches are considered costly and time consuming. The need for a reliable analytical approach that can be used for examining wind-effects on buildings is clear. Although Computational Fluid Dynamics (CFD) is one of the other alternative numerical options yet might not reached the level of confidence to be reliably used to finalize the structural design. On the other hand, a limited number of studies have been carried out using soft computing methods to examine wind-induced loads on structures. However, its promising results, more work is still required towards achieving the full analytical prediction of wind effects on structures. This study investigates the use of different soft-computing techniques in predicting wind pressures on tall buildings. Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. Wind tunnel datasets, used in the current study to develop the proposed computing models, were collected from testing three tall buildings having the same full-scale horizontal dimensions of (40 m and 80 m) and different heights of (80 m, 120 m and 160 m). The buildings were tested at a scale of 1:400 in urban terrain exposure. Mean and fluctuating wind pressure coefficients on the building with the height of 120 m are herein predicted using the seven computing methods and the results were compared to the corresponding measured pressures. Overall, the examined methods performed well in the wind pressure prediction process. Furthermore, the employed DNN was found to have the best performance in predicting mean and fluctuating wind pressures with the highest correlation coefficients. Hence, the DNN was also used in predicting the mean and fluctuating wind pressures on the two other buildings with heights of 80 m and 160 m. Experimental results indicate that the employed DNN model can be effectively used in predicting wind-induced pressures on tall buildings. |
| format | Article |
| id | doaj-art-3110fefc08af4f779ffb13a2a6b56598 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-3110fefc08af4f779ffb13a2a6b565982025-01-18T05:03:37ZengElsevierAlexandria Engineering Journal1110-01682025-01-01111610627Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildingsMosbeh R. Kaloop0Abidhan Bardhan1Pijush Samui2Jong Wan Hu3Mohamed Elsharawy4Department of Civil and Environmental Engineering, Incheon National University, South Korea; Incheon Disaster Prevention Research Center, Incheon National University, South Korea; Public Works and Civil Engineering Department, Mansoura University, EgyptDepartment of Civil Engineering, National Institute of Technology Patna, IndiaDepartment of Civil Engineering, National Institute of Technology Patna, IndiaDepartment of Civil and Environmental Engineering, Incheon National University, South Korea; Incheon Disaster Prevention Research Center, Incheon National University, South Korea; Corresponding author at: Department of Civil and Environmental Engineering, Incheon National University, South Korea.Structural Engineering Department, Mansoura University, EgyptWind-structures interaction has been extensively examined in the last few decades using field measurements, full scale measurements and wind tunnel testing. These experimental approaches are considered costly and time consuming. The need for a reliable analytical approach that can be used for examining wind-effects on buildings is clear. Although Computational Fluid Dynamics (CFD) is one of the other alternative numerical options yet might not reached the level of confidence to be reliably used to finalize the structural design. On the other hand, a limited number of studies have been carried out using soft computing methods to examine wind-induced loads on structures. However, its promising results, more work is still required towards achieving the full analytical prediction of wind effects on structures. This study investigates the use of different soft-computing techniques in predicting wind pressures on tall buildings. Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. Wind tunnel datasets, used in the current study to develop the proposed computing models, were collected from testing three tall buildings having the same full-scale horizontal dimensions of (40 m and 80 m) and different heights of (80 m, 120 m and 160 m). The buildings were tested at a scale of 1:400 in urban terrain exposure. Mean and fluctuating wind pressure coefficients on the building with the height of 120 m are herein predicted using the seven computing methods and the results were compared to the corresponding measured pressures. Overall, the examined methods performed well in the wind pressure prediction process. Furthermore, the employed DNN was found to have the best performance in predicting mean and fluctuating wind pressures with the highest correlation coefficients. Hence, the DNN was also used in predicting the mean and fluctuating wind pressures on the two other buildings with heights of 80 m and 160 m. Experimental results indicate that the employed DNN model can be effectively used in predicting wind-induced pressures on tall buildings.http://www.sciencedirect.com/science/article/pii/S111001682401202XWind pressureWind loadsWind tunnelMachine learningDeep learningArtificial intelligence |
| spellingShingle | Mosbeh R. Kaloop Abidhan Bardhan Pijush Samui Jong Wan Hu Mohamed Elsharawy Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings Alexandria Engineering Journal Wind pressure Wind loads Wind tunnel Machine learning Deep learning Artificial intelligence |
| title | Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| title_full | Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| title_fullStr | Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| title_full_unstemmed | Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| title_short | Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| title_sort | comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings |
| topic | Wind pressure Wind loads Wind tunnel Machine learning Deep learning Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S111001682401202X |
| work_keys_str_mv | AT mosbehrkaloop comparativestudyondeepandmachinelearningapproachesforpredictingwindpressuresontallbuildings AT abidhanbardhan comparativestudyondeepandmachinelearningapproachesforpredictingwindpressuresontallbuildings AT pijushsamui comparativestudyondeepandmachinelearningapproachesforpredictingwindpressuresontallbuildings AT jongwanhu comparativestudyondeepandmachinelearningapproachesforpredictingwindpressuresontallbuildings AT mohamedelsharawy comparativestudyondeepandmachinelearningapproachesforpredictingwindpressuresontallbuildings |