Multivariate adaptive regression spline approach to the assessment of surface mean pressure coefficient on surfaces of C-shaped building

Document Type : Research Note

Authors

Department of Civil Engineering, National Institute of Technology Rourkela, Odisha, India-769008

Abstract

Proper assessment of wind load enables durable design of structures under varying wind load conditions. The accurate prediction of pressure coefficient on any irregular plan shaped buildings is essential for the assessment of wind loads and the structural design. The main objective of this study is to present an equation in the line of Multivariate Adaptive Regression Spline (MARS) approach using experimental data of surface mean pressure coefficient. This developed equation can be used satisfactorily for the prediction of pressure coefficient values accurately on the surfaces of C-shaped buildings. An extensive experimentation was carried out to obtain coefficient of pressure over the surfaces of C-shaped models under varying sizes, corner curvature and angle of incidence in a sub-sonic wind tunnel. The predicted values of pressure coefficient of different C-shaped buildings using developed model are compared with equations developed by Swami and Chandra’s (S&C) and Muehleisen and Patrizi’s (M&P). The comparison indicates that the proposed MARS model predicts pressure coefficient values more accurately than those by S&C and M&P models on frontal as well as side surfaces. Further, the model is used to validate with the actual building, Tokyo Polytechnic University (TPU) data to show the applicability of the proposed equation.

Keywords


References
1. Lin, N., Letchford, C., Tamura, Y., Liang, B., and
Nakamura, O. Characteristics of wind forces acting
on tall buildings", Journal of Wind Engineering and
Industrial Aerodynamics, 93(3), pp. 217{242 (2005).
2. Macdonald, A.J., Wind Loading on Buildings, Halsted
Press (1975).
3. Meroney, R.N. Wind-tunnel modelling of the
ow
about blu bodies", Journal of Wind Engineering and
Industrial Aerodynamics, 29(1{3), pp. 203{223 (1988).
4. Cook, N.J. The designer's guide to wind loading of
building structures", Building Research Establishment
Report, Part 2: Static Structures, Butterworths, London
(1990).
5. Suresh Kumar, K., Irwin, P.A., and Davies, A.
Design of tall building for wind: wind tunnel vs.
codes/standards", Third National Conference on Wind
Engineering, Calcutta, India, pp. 318{325 (2006).
6. Stathopoulos, T. and Zhou, Y.S. Numerical simulation
of wind-induced pressures on buildings of various
geometries", Computational Wind Engineering, 1, Elsevier,
pp. 419{430 (1993).
7. Zhou, Y. and Stathopoulos, T. A new technique for
the numerical simulation of wind
ow around buildings",
Journal of Wind Engineering and Industrial
Aerodynamics, 72, pp. 137{147 (1997).
8. Ahmad, S. and Kumar, K. Interference e ects on
wind loads on low-rise hip roof buildings", Engineering
Structures, 23(12), pp. 1577{1589 (2001).
9. Ho, T.C.E., Surry, D., and Davenport, A.G. The
variability of low building wind loads due to surrounding
obstructions", Journal of Wind Engineering and
Industrial Aerodynamics, 36, pp. 161{170 (1990).
10. Lou, W., Jin, H., Chen, Y., Cao, L., and Yao, J. Wind
tunnel test study on wind load characteristics for
double-skin facade building with rectangular shape",
Journal of Building Structure, 26(1), pp. 65{70 (2005).
2982 M. Mallick et al./Scientia Iranica, Transactions B: Mechanical Engineering 27 (2020) 2967{2984
11. Lu, S., Chen, S.F., Li, J.H., and Jiao, Y.F. Numerical
study on the e ects of curved annex on the wind loads
on a spherical tall building", Engineering Mechanics,
2, p. 021 (2007).
12. Chakraborty, S., Dalui, S.K., and Ahuja, A.K. Experimental
and numerical study of surface pressure on
'+' plan shape tall building", International Journal of
Construction Materials and Structures, 8(3), pp. 251{
262 (2013).
13. Gomes, M.G.R., Rodrigues, A.M., and Mendes, P.
Experimental and numerical study of wind pressures
on irregular-plan shapes", Journal of Wind Engineering
and Industrial Aerodynamics, 93(10), pp. 741{756
(2005).
14. Amin, J.A. and Ahuja, A.K. Experimental study
of wind pressures on irregular plan shape buildings",
BBAA VI International Colloquium on: Blu Bodies
Aerodynamics and Applications, Milano, Italy, pp. 20{
24 (2008).
15. Amin, J.A. and Ahuja, A.K. Experimental study
of wind-induced pressures on buildings of various
geometries", International Journal of Engineering,
Science and Technology, 3(5), pp. 1{19 (2011).
https://www.ajol.info/index.php/ijest/issue/view/
8314
16. Kim, Y.C. and Kanda, J. Wind pressures on tapered
and set-back tall buildings", Journal of Fluids and
Structures, 39, pp. 306{321 (2013).
17. Chakraborty, S., Dalui, S.K., and Ahuja, A.K. Wind
load on irregular plan shaped tall building-a case
study", Wind Struct., 19(1), pp. 59{73 (2014).
18. Bhattacharyya, B., Dalui, S.K., and Ahuja, A.K.
Wind induced pressure on E plan shaped tall buildings",
Jordon Journal of Civil Engineering, 8(2), pp.
120{134 (2014).
19. Bhattacharyya, B. and Dalui, S.K. Investigation of
mean wind pressures on 'E' plan shaped tall building",
Wind and Structures, 26(2), pp. 99{114 (2018).
20. Yi, J. and Li, Q.S. Wind tunnel and full-scale study of
wind e ects on a super-tall building", Journal of Fluid
Structure, 58, pp. 236{253 (2015).
21. Li, Y. and Li, Q. Across-wind dynamic loads on
L-shaped tall buildings", Wind Structure, 23(5), pp.
385{403 (2016).
22. Mallick, M., Mohanta, A., Kumar, A., and Raj,
V. Modelling of wind pressure coecients on Cshaped
building models", Modelling and Simulation
in Engineering, 2018, Article ID 6524945, 13 pages
(2018). https://doi.org/10.1155/2018/6524945
23. Akins, R.E. Wind pressures on buildings", CER;
76/77-15 (1976).
24. Walton, G.N. Air
ow and multiroom thermal analysis",
ASHRAE Transactions, 88, pp. 78{91 (1982).
25. Walker, I.S. and Wilson, D.J. Evaluating models for
superposition of wind and stack e ect in air in ltration",
Building and Environment, 28(2), pp. 201{210
(1993).
26. Ginger, J.D. and Letchford, C.W. Net pressures
on a low-rise full-scale building", Journal of Wind
Engineering and Industrial Aerodynamics, 83(1{3),
pp. 239{250 (1999).
27. Ohkuma, T., Marukawa, H., Niihori, Y., and Kato,
N. Full-scale measurement of wind pressures and
response accelerations of a high-rise building", Journal
of Wind Engineering and Industrial Aerodynamics,
38(2{3), pp. 185{196 (1991).
28. Swami, M.V. and Chandra, S. Procedures for calculating
natural ventilation air
ow rates in buildings",
ASHRAE Final Report FSEC-CR-163-86, ASHRAE
Research Project (1987).
29. Swami, M.V. and Chandra, S. Correlations for
pressure distribution on buildings and calculation of
natural-ventilation air
ow", ASHRAE Transactions,
94(3112), pp. 243{266 (1988).
30. Grosso, M. Wind pressure distribution around buildings:
a parametrical model", Energy and Buildings,
18(2), pp. 101{131 (1992).
31. Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., Buhl,
W.F., Huang, Y.J., Pedersen, C.O., Strand, R.K.,
Liesen, R.J., Fisher, D.E., and Witte, M.J. EnergyPlus:
creating a new-generation building energy
simulation program", Energy and Buildings, 33(4), pp.
319{331 (2001).
32. Cook, N., Designers' Guide to EN 1991-1-4 Eurocode
1: Actions on Structures, general actions part 1-4.
Wind actions, Thomas Telford Publishing (2007).
33. Costola, D., Blocken, B., and Hensen, J.L.M.
Overview of pressure coecient data in building
energy simulation and air
ow network programs",
Building and Environment, 44(10), pp. 2027{2036
(2009).
34. Costola, D., Blocken, B., Ohba, M., and Hensen,
J.L.M. Uncertainty in air
ow rate calculations due
to the use of surface-averaged pressure coecients",
Energy and Buildings, 42(6), pp. 881{888 (2010).
35. Muehleisen, R.T. and Patrizi, S. A new parametric
equation for the wind pressure coecient for low-rise
buildings", Energy and Buildings, 57, pp. 245{249
(2013).
36. Deo, R.C. and Sahin, M. Application of the extreme
learning machine algorithm for the prediction of
monthly e ective drought index in eastern Australia",
Atmospheric Research, 153, pp. 512{525 (2015).
37. Samadi, M., Jabbari, E., Azamathulla, H.M., and Mojallal,
M. Estimation of scour depth below free overfall
spillways using multivariate adaptive regression splines
and arti cial neural networks", Engineering Applications
of Computational Fluid Mechanics, 9(1), pp.
291{300 (2015).
38. Suman, S., Mahamaya, M., and Das, S.K. Prediction
of maximum dry density and uncon ned compressive
strength of cement stabilised soil using arti cial intelligence
techniques", International Journal of Geosynthetics
and Ground Engineering, 2(2), p. 11 (2016).
M. Mallick et al./Scientia Iranica, Transactions B: Mechanical Engineering 27 (2020) 2967{2984 2983
39. Mehdizadeh, S., Behmanesh, J., and Khalili, K. Application
of gene expression programming to predict
daily dew point temperature", Applied Thermal Engineering,
112, pp. 1097{1107 (2017).
40. Milukow, H.A., Binns, A.D., Adamowski, J.,
Bonakdari, H., and Gharabaghi, B. Estimation of the
darcy-weisbach friction factor for ungauged streams
using gene expression programming and extreme learning
machines", Journal of Hydrology, 568, pp. 311{321
(2018).
41. Mohanta, A., Patra, K.C., and Sahoo, B. Anticipate
Manning's coecient in meandering compound channels",
Hydrology, 5(3), p. 47 (2018).
42. Najafzadeh, M., Rezaie-Balf, M., and Tafarojnoruz,
A. Prediction of riprap stone size under overtopping

ow using data-driven models", International Journal
of River Basin Management, 16(4), pp. 1{8 (2018).
43. Shende, S. and Chau, K.W. Forecasting safe distance
of a umping well for e ective riverbank ltration",
Journal of Hazardous, Toxic, and Radioactive Waste,
23(2), p. 04018040 (2018).
44. Varvani, J. and Khaleghi, M.R. A performance
evaluation of neuro-fuzzy and regression methods
in estimation of sediment load of selective
rivers", Acta Geophysica, 67(1), pp. 205{214 (2018).
https://doi.org/10.1007/s11600-018-0228-9
45. Sheikh Khozani, Z., Bonakdari, H., and Zaji, A.H.
Mean bed shear stress estimation in a rough rectangular
channel using a hybrid genetic algorithm based on
an arti cial neural network and genetic programming",
Scientia Iranica, 25(1), pp. 152{161 (2018).
46. Bui, D.T., Hoang, N.D., and Samui, P. Spatial
pattern analysis and prediction of forest re using new
machine learning approach of multivariate adaptive
regression splines and di erential
ower pollination
optimization: a case study", at Lao Cai province (Viet
Nam). Journal of Environmental Management, 237,
pp. 476{487 (2019).
47. Zaji, A.H., Bonakdari, H., and Shamshirband, S.
Standard equations for predicting the discharge coe
cient of a modi ed high-performance side weir",
Scientia Iranica, 25(3), pp. 1057{1069 (2018).
48. Samui, P., Das, S., and Kim, D. Uplift capacity
of suction caisson in clay using multivariate adaptive
regression spline", Ocean Engineering, 38(17{18), pp.
2123{2127 (2011).
49. Samui, P. and Kurup, P. Multivariate adaptive regression
spline (MARS) and least squares support
vector machine (LSSVM) for OCR prediction", Soft
Computing, 16(8), pp. 1347{1351 (2012).
50. Samui, P. Multivariate adaptive regression spline
(Mars) for prediction of elastic modulus of jointed
rock mass", Geotechnical and Geological Engineering,
31(1), pp. 249{253 (2013).
51. Cheng, M.Y. and Cao, M.T. Accurately predicting
building energy performance using evolutionary
multivariate adaptive regression splines", Applied Soft
Computing, 22, pp. 178{188 (2014).
52. Koc, E.K. and Bozdogan, H. Model selection in
multivariate adaptive regression splines (MARS) using
information complexity as the tness function", Machine
Learning, 101(1{3), pp. 35{58 (2015).
53. Zhang, W. and Goh, A.T. Multivariate adaptive
regression splines and neural network models for prediction
of pile drivability", Geoscience Frontiers, 7(1),
pp. 45{52 (2016).
54. Mukhopadhyay, T. A multivariate adaptive regression
splines based damage identi cation methodology
for web core composite bridges including
the e ect of noise", Journal of Sandwich Structures
& Materials, 20(7), pp. 885{903 (2017).
https://doi.org/10.1177/1099636216682533
55. Bhattacharya, S., Murakonda, P., and Das, S. Prediction
of uplift capacity of suction caisson in clay
using functional network and multivariate adaptive
regression spline", Scientia Iranica, 25(2), pp. 517{531
(2018).
56. Mirabbasi, R., Kisi, O., Sanikhani, H., and
Meshram, S.G. Monthly long-term rainfall estimation
in Central India using M5Tree, MARS,
LSSVR, ANN and GEP models", Neural Computing
and Applications, 31(10), pp. 6843{6862 (2019).
https://doi.org/10.1007/s00521-018-3519-9
57. Goh, A.T.C., Zhang, W., Zhang, Y., Xiao, Y., and
Xiang, Y. Determination of earth pressure balance
tunnel-related maximum surface settlement: a multivariate
adaptive regression splines approach", Bulletin
of Engineering Geology and the Environment, 77(2),
pp. 489{500 (2018).
58. Zhang, W., Zhang, R., and Goh, A.T. Multivariate
adaptive regression splines approach to estimate lateral
wall de
ection pro les caused by braced excavations
in clays", Geotechnical and Geological Engineering,
36(2), pp. 1349{1363 (2018).
59. Friedman, J.H. Multivariate adaptive regression
splines", Annals of Statistics, 19(1), pp. 1{67 (1991).
https://doi.org/10.1214/aos/1176347963
60. Friedman, J.H. and Roosen, C.B., An Introduction to
Multivariate Adaptive Regression Splines, Sage Publications
Sage CA: Thousand Oaks, CA (1995).
61. Leathwick, J.R., Rowe, D., Richardson, J., Elith,
J., and Hastie, T. Using multivariate adaptive regression
splines to predict the distributions of New
Zealand's freshwater diadromous sh", Freshwater Biology,
50(12), pp. 2034{2052 (2005).
62. Craven, P. andWahba, G. Smoothing noisy data with
spline functions", Numerische Mathematik, 31(4), pp.
377{403 (1978).
63. Barati, R., Rahimi, S., and Akbari, G.H. Analysis
of dynamic wave model for
ood routing in natural
rivers", Water Science and Engineering, 5(3), pp. 243{
258 (2012).
2984 M. Mallick et al./Scientia Iranica, Transactions B: Mechanical Engineering 27 (2020) 2967{2984
64. Barati, R. Application of excel solver for parameter
estimation of the nonlinear Muskingum models",
KSCE Journal of Civil Engineering, 17(5), pp. 1139{
1148 (2013).
65. Akbari, G.H. and Barati, R. Comprehensive analysis
of
ooding in unmanaged catchments", Proceedings of
the Institution of Civil Engineers-Water Management,
pp. 229{238 (2012).
66. Gandomi, A.H., Yun, G.J., and Alavi, A.H. An
evolutionary approach for modelling of shear strength
of RC deep beams", Materials and Structures, 46(12),
pp. 2109{2119 (2013).
67. Ebtehaj, I., Bonakdari, H., Zaji, A.H., Azimi, H., and
Khoshbin, F. GMDH-type neural network approach
for modeling the discharge coecient of rectangular
sharp-crested side weirs", Engineering Science and
Technology, an International Journal, 18(4), pp. 746{
757 (2015).
68. Bre, F., Gimenez, J.M., and Fachinotti, V.D. Prediction
of wind pressure coecients on building surfaces
using arti cial neural networks", Energy and Buildings,
158, pp. 1429{1441 (2018).