Browse > Article
http://dx.doi.org/10.12989/was.2021.32.2.127

Estimation of wind pressure coefficients on multi-building configurations using data-driven approach  

Konka, Shruti (Department of Civil Engineering, BITS Pilani, Hyderabad Campus)
Govindray, Shanbhag Rahul (Department of Mechanical Engineering, BITS Pilani, Hyderabad Campu)
Rajasekharan, Sabareesh Geetha (Department of Mechanical Engineering, BITS Pilani, Hyderabad Campu)
Rao, Paturu Neelakanteswara (Department of Civil Engineering, BITS Pilani, Hyderabad Campus)
Publication Information
Wind and Structures / v.32, no.2, 2021 , pp. 127-142 More about this Journal
Abstract
Wind load acting on a standalone structure is different from that acting on a similar structure which is surrounded by other structures in close proximity. The presence of other structures in the surrounding can change the wind flow regime around the principal structure and thus causing variation in wind loads compared to a standalone case. This variation on wind loads termed as interference effect depends on several factors like terrain category, geometry of the structure, orientation, wind incident angle, interfering distances etc., In the present study, a three building configuration is considered and the mean pressure coefficients on each face of principle building are determined in presence of two interfering buildings. Generally, wind loads on interfering buildings are determined from wind tunnel experiments. Computational fluid dynamic studies are being increasingly used to determine the wind loads recently. Whereas, wind tunnel tests are very expensive, the CFD simulation requires high computational cost and time. In this scenario, Artificial Neural Network (ANN) technique and Support Vector Regression (SVR) can be explored as alternative tools to study wind loads on structures. The present study uses these data-driven approaches to predict mean pressure coefficients on each face of principle building. Three typical arrangements of three building configuration viz. L shape, V shape and mirror of L shape arrangement are considered with varying interfering distances and wind incidence angles. Mean pressure coefficients (Cp mean) are predicted for 45 degrees wind incidence angle through ANN and SVR. Further, the critical faces of principal building, critical interfering distances and building arrangement which are more prone to wind loads are identified through this study. Among three types of building arrangements considered, a maximum of 3.9 times reduction in Cp mean values are noticed under Case B (V shape) building arrangement with 2.5B interfering distance. Effect of interfering distance and building arrangement on suction pressure on building faces has also been studied. Accordingly, Case C (mirror of L shape) building arrangement at a wind angle of 45º shows less suction pressure. Through this study, it was also observed that the increase of interfering distance may increase the suction pressure for all the cases of building configurations considered.
Keywords
mean pressure coefficient; Artificial Neural Network (ANN); three building configuration;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Khanduri, A.C., Stathopoulos T. and Bedard C. (1998), "Wind-induced interference effects on buildings-a review of the state-of-the-art", Eng. Struct., 20(7), 617-30. https://doi.org/10.1016/S0141-0296(97)00066-7.   DOI
2 Khanduri, A.C., Stathopoulos T. and Bedard C. (2000), "Generalization of wind-induced interference effects for two buildings", Wind Struct., 3(4), 255-266. https://doi.org/10.12989/was.2000.3.4.255.   DOI
3 Kim, W., Tamura, Y. and Yoshida, A. (2015), "Interference effects on aerodynamic wind forces between two buildings", J. Wind Eng. Ind. Aerod., 147, 186-201. https://doi.org/10.1016/j.jweia.2015.10.009.   DOI
4 Krizan, J., Gasparac, G., Kozmar, H., Antonic, O. and Grisogono, B. (2015), "Designing laboratory wind simulations using artificial neural networks. Theoretical and applied climatology", 120(3), 723-736. https://doi.org/10.1007/s00704-014-1201-4.   DOI
5 Kumar, Suresh. K and Ted, S. (2001), "Generation of local wind pressure coefficients for the design of low building roofs", Wind Struct., 4(6), 465-468. https://doi.org/10.12989/was.2001.4.6.455.   DOI
6 Kwatra, N., Godbole, P.N. and Krishna, P. (2002), "Application of artificial neural network for determination of wind induced pressures on gable roof", Wind Struct., 5(1), 1-14. https://doi.org/10.12989/was.2002.5.1.001.   DOI
7 Mara, T.G., Terry B.K., Ho T.C. and Isyumov, N. (2014), "Aerodynamic and peak response interference factors for an upstream square building of identical height", J. Wind Eng. Ind. Aerod., 133, 200-210. https://doi.org/10.1016/j.jweia.2014.06.010.   DOI
8 Mendis, P., Ngo, T., Haritos, N., Hira, A., Samali, B. and Cheung, J. (2007), "Wind loading on tall buildings", Electron. J. Struct. Eng., 7, 41-54. http://hdl.handle.net/10453/5822.   DOI
9 Meng, F.Q., He, B.J., Zhu, J., Zhao, D.X., Darko, A. and Zhao, Z. Q. (2018), "Sensitivity analysis of wind pressure coefficients on CAARC standard tall buildings in CFD simulations", J. Build. Eng., 16, 146-158. https://doi.org/10.1016/j.jobe.2018.01.004.   DOI
10 Nikose, T.J. and Sonparote, R.S. (2019), "Dynamic along wind response of tall buildings using Artificial Neural Network", Cluster Comput., 22(2), 3231-3246. https://doi.org/10.1007/s10586-018-2027-0.   DOI
11 Nikose, T.J. and Sonparote, R.S. (2019), "Dynamic wind response of tall buildings using artificial neural network", Struct. Des. Tall Special Build., 28(13), e1657. https://doi.org/10.1002/tal.1657.   DOI
12 Tamura, T., Nozawa K. and Kondo K. (2008), "AIJ guide for numerical prediction of wind loads on buildings", J. Wind Eng. Ind. Aerod., 96(10-11), 1749-1761. https://doi.org/10.1016/j.jweia.2008.02.058.   DOI
13 Sandri, P. (1996), An artificial neural network for wind-induced damage potential to nonengineered buildings, Ph.D. Dissertation, Texas Tech University, Texas.
14 Shruti, K., Sabareesh, G.R. and Raoa, P.N. (2013), "A comparative study on interference factors of buildings", The Eighth Asia-Pacific Conference on Wind Engineering, Chennai, India, December.
15 Shruti, K., Sabareesh, G.R., Rao, P.N. and Pilani, B.I.T.S. (2018), "Mitigating wind induced disasters on a group of buildings and cooling towers due to interference effect", In Proc of International Workshop on Wind Related disasters and mitigation, Sendai, Japan.
16 TPU Aerodynamic Data base: Wind Pressure Database of Two Adjacent Tall Buildings. (2013), Wind Engineering Research Center, Tokyo Polytechnic University, Japan. http://www.wind.arch.tkougei.ac.jp/system/eng/contents/code/tpu.
17 Wang, J. and Cheng C.M. (2010), "The application of artificial neural networks to predict wind spectra for rectangular cross-section buildings", In Proceedings of Fifth International Symposium on Computational Wind Engineering (CWE2010), Chapel Hill, North Carolina, May.
18 Wijesooriya, K., Mohotti, D., Chauhan, K. and Dias-da-Costa, D. (2019), "Numerical investigation of scale resolved turbulence models (LES, ELES and DDES) in the assessment of wind effects on supertall structures", J. Build. Eng., 25, 100842. https://doi.org/10.1016/j.jobe.2019.100842.   DOI
19 Zhao, J.G. and K.M. Lam. (2008), "Interference effects in a group of tall buildings closely arranged in an L- or T-shaped pattern", Wind Struct., 11(1), 1-18. https://doi.org/10.12989/was.2008.11.1.001.   DOI
20 Zu, G.B. and K.M. Lam. (2018), "Shielding effects on a tall building from a row of low and medium rise buildings", Wind Struct., 27(6), 439-449. https://doi.org/10.12989/was.2018.27.6.439.   DOI
21 English, E.C. and Fricke F.R. (1999), "The interference index and its prediction using a neural network analysis of wind-tunnel data", J. Wind Eng. Ind. Aerod., 83(1-3), 567-75.https://doi.org/10.1016/S0167-6105(99)00102-6.   DOI
22 Amin, J.A. and Ahuja, A. (2012), "Wind-induced mean interference effects between two closed spaced buildings", KSCE J. Civil Eng., 16(1), 119-131.https://doi.org/10.1007/s12205-012-1163-y.   DOI
23 Bre, F., Gimenez J.M. and Fachinotti V.D. (2018), "Prediction of wind pressure coefficients on building surfaces using artificial neural networks", Energy Build., 158, 1429-41. https://doi.org/10.1016/j.enbuild.2017.11.045.   DOI
24 Chen, J., Xue, X., Ha, M., Yu, D. and Ma, L. (2014), "Support vector regression method for wind speed prediction incorporating probability prior knowledge", Mathem. Prob. Eng., 2014, 410489. https://doi.org/10.1155/2014/410489.   DOI
25 Dagnew, A. and Bitsuamlak, G.T. (2013), "Computational evaluation of wind loads on buildings: a review", Wind Struct., 16(6), 629-660. https://doi.org/10.12989/was.2013.16.6.629.   DOI
26 Elshaer, A., Gairola A., Adamek K. and Bitsuamlak G. (2017), "Variations in wind load on tall buildings due to urban development", Sustain. Cities Soc, 34, 264-277. https://doi.org/10.1016/j.scs.2017.06.008.   DOI
27 Gavalda, X., Ferrer-Gener, J., Kopp, G.A. and Giralt, F. (2011), "Interpolation of pressure coefficients for low-rise buildings of different plan dimensions and roof slopes using artificial neural networks", J. Wind Eng. Ind. Aerod., 99(5), 658-664. https://doi.org/10.1016/j.jweia.2011.02.008.   DOI
28 Gu, M., Xie Z.N. and Huang P. (2005), "Along-wind dynamic interference effects of tall buildings", Advan. Struct. Eng., 8(6), 623-636. https://doi.org/10.1260%2F136943305776318400.   DOI
29 Gu, Ming. and Zhuang-Ning Xie (2011), "Interference effects of two and three super-tall buildings under wind action", Acta Mech. Sin., 27(5), 687-696. https://doi:10.1007/s10409-011-0498-9.   DOI
30 Hu, G., Liu, L., Tao, D., Song, J., Tse, K.T. and Kwok, K.C.S., (2020), "Deep learning-based investigation of wind pressures on tall building under interference effects", J. Wind Eng. Ind. Aerod., 201, 104138. https://doi.org/10.1016/j.jweia.2020.104138.   DOI
31 Khanduri, A.C., Bedard C. and Stathopoulos T. (1997), "Modelling wind-induced interference effects using backpropagation neural networks", J. Wind Eng. Ind. Aerod., 72, 71-79. https://doi.org/D.   DOI