ORIGINAL_ARTICLE
Determining the Drift in Reinforced Concrete Building Using ANFIS Soft Computing Modeling
Earthquakes are considered as one of the most significant natural disasters that can potentially cause significant damages to structures. Displacement of buildings’ floors is one of the serious failures in structures caused by earthquakes. In this paper, the drift of a concrete frame with the shear wall is estimated using ANFIS modeling. A dataset of 300 measured data points was used herein as the inputs for the ANFIS model. The dataset has totally six input parameters including frequency, magnitude, peak ground acceleration (PGA), and shear wave velocity (Vs) of an earthquake and the distance from the earthquake epicenter to use in the ANFIS model, while the model has just one output. Moreover, a sensitivity analysis was performed on the dataset in order to determine the efficiency of the individual input variables on the accuracy of the results. The results demonstrate that the ANFIS model is an effective model for predicting the drift in reinforced concrete structures. Finally, according to sensitivity analysis, the acceleration and shear wave velocity of an earthquake have the highest and lowers impacts on the accuracy of the results, respectively.
https://www.jcepm.com/article_53677_27872cfe358bae8bc483bc892476b788.pdf
2018-01-01
1
11
10.22115/cepm.2018.53677
Displacement
drift
ANFIS
Concrete
Data-driven models
Hojjatollah
Torkian
hodjat.torkian@gmail.com
1
Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
AUTHOR
Zahra
Keshavarz
zahrakeshavarz.88@gmail.com
2
Department of Civil Engineering, Islamshahr Branch, Islamic Azad University Islamshahr, Iran
LEAD_AUTHOR
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[6] Lin Z Bin, Azarmi F, Al-Kaseasbeh Q, Azimi M, Yan F. Advanced Ultrasonic Testing Technologies with Applications to Evaluation of Steel Bridge Welding - An Overview. Appl Mech Mater 2015;727–728:785–9. doi:10.4028/www.scientific.net/AMM.727-728.785.
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[13] Khademi F, Jamal SM, Deshpande N, Londhe S. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. Int J Sustain Built Environ 2016;5:355–69. doi:10.1016/j.ijsbe.2016.09.003.
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[17] Khademi F, Jamal SM. Estimating the compressive strength of concrete using multiple linear regression and adaptive neuro-fuzzy inference system. Int J Struct Eng 2017;8:20. doi:10.1504/IJSTRUCTE.2017.081669.
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[19] Khademi F, Akbari M, Jamal SM. Prediction of concrete compressive strength using ultrasonic pulse velocity test and artificial neural network modeling. Rom J Mater 2016;46:343–50.
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[20] Keshavarz Z. Predicting the Civil Engineering Characteristics through Soft Computing Models. Civ Eng Res J 2017;1. doi:10.19080/CERJ.2017.01.555563.
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[21] Keshavarz Z, Torkian H. Application of ANN and ANFIS Models in Determining Compressive Strength of Concrete. J Soft Comput Civ Eng 2018;2:62–70. doi:10.22115/SCCE.2018.51114.
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[22] Nikoo M, Zarfam P. Determining Confidence for Evaluation of Vulnerability In Reinforced Concrete Frames with Shear Wall. J Basic Appl Sci Res 2012;2:6605–14.
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[23] Nikoo M, Zarfam P. Determining Displacement in Concrete Reinforcement Building with using Evolutionary Artificial Neural Networks. World Appl Sci J 2012;16:1699–708.
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[24] Khademi F, Akbari M, Nikoo M. Displacement determination of concrete reinforcement building using data-driven models. Int J Sustain Built Environ 2017;6:400–11. doi:10.1016/j.ijsbe.2017.07.002.
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[25] Lee S, Lee C. Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Eng Struct 2014;61:99–112. doi:10.1016/j.engstruct.2014.01.001.
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[26] Khademi F, Jamal SM. Predicting the 28 days compressive strength of concrete using artificial neural network. I-Manager’s J Civ Eng 2016;6:1.
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[27] Mansouri I, Kisi O. Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos Part B Eng 2015;70:247–55. doi:10.1016/j.compositesb.2014.11.023.
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[28] Madandoust R, Bungey JH, Ghavidel R. Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Comput Mater Sci 2012;51:261–72. doi:10.1016/j.commatsci.2011.07.053.
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[29] KhalilzadeVahidi E, Rahimi F. Investigation of Ultimate Shear Capacity of RC Deep Beams with Opening using Artificial Neural Networks. Adv Comput Sci an Int J 2016;5:57–65.
29
[30] Khademi F, Akbari M, Jamal SM, Nikoo M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng 2017;11:90–9. doi:10.1007/s11709-016-0363-9.
30
[31] Naderpour H, Kheyroddin A, Amiri GG. Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Compos Struct 2010;92:2817–29. doi:10.1016/j.compstruct.2010.04.008.
31
[32] Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models. Constr Build Mater 2010;24:709–18. doi:10.1016/j.conbuildmat.2009.10.037.
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[33] Nazari A, Khalaj G. Prediction compressive strength of lightweight geopolymers by ANFIS. Ceram Int 2012;38:4501–10. doi:10.1016/j.ceramint.2012.02.026.
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[34] Boğa AR, Öztürk M, Topçu İB. Using ANN and ANFIS to predict the mechanical and chloride permeability properties of concrete containing GGBFS and CNI. Compos Part B Eng 2013;45:688–96. doi:10.1016/j.compositesb.2012.05.054.
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[35] Zhou Q, Wang F, Zhu F. Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems. Constr Build Mater 2016;125:417–26. doi:10.1016/j.conbuildmat.2016.08.064.
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[36] Behfarnia K, Khademi F. A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system. Int J Optim Civ Eng 2017;7:71–80.
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[37] Sadeghi-Nik A, Berenjian J, Bahari A, Safaei AS, Dehestani M. Modification of microstructure and mechanical properties of cement by nanoparticles through a sustainable development approach. Constr Build Mater 2017;155:880–91. doi:10.1016/j.conbuildmat.2017.08.107.
37
[38] Kafi MA, Sadeghi-Nik A, Bahari A, Sadeghi-Nik A, Mirshafiei E. Microstructural Characterization and Mechanical Properties of Cementitious Mortar Containing Montmorillonite Nanoparticles. J Mater Civ Eng 2016;28:4016155. doi:10.1061/(ASCE)MT.1943-5533.0001671.
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[39] Bahari A, Sadeghi-Nik A, Roodbari M, Taghavi K, Mirshafiei E. Synthesis and strength study of cement mortars containing SiC nano particles. vol. 7. 2012.
39
[40] Bahari A, Berenjian J, Sadeghi-Nik A. Modification of Portland Cement with Nano SiC. Proc Natl Acad Sci India Sect A Phys Sci 2016;86:323–31. doi:10.1007/s40010-015-0244-y.
40
[41] Bahari A, Sadeghi Nik A, Roodbari M, Mirshafiei E, Amiri B. Effect of Silicon Carbide Nano Dispersion on the Mechanical and Nano Structural Properties of Cement. Natl Acad Sci Lett 2015;38:361–4. doi:10.1007/s40009-014-0316-6.
41
[42] MOSAVI SM, SADEGHI NIK A. Strengthening of steel–concrete composite girders using carbon fibre reinforced polymer (CFRP) plates. Sadhana 2015;40:249–61. doi:10.1007/s12046-014-0294-x.
42
ORIGINAL_ARTICLE
Numerical Simulation of Concrete Mix Structure and Detection of its Elastic Stiffness
Concrete mix stiffness (CM) primarily relies on its ingredients, which mainly consists of stone aggregate and mortar. To analyse the role of the components of CM on its properties a numerical simulation of CM structure is conducted. Within the scope of this study, the structure and the properties of CM are simulated using ANSYS code to apply the finite element method (FEM). The size of aggregate is modelled using direct random nodes and elements and the problem is approximated as two-dimensional plane one. Different ratios of aggregate and mortar were considered to determine their influence on the stiffness of CM. The CM is treated as bi-composite and subjected to compressive loading. For determining the influence of the proportion of stone aggregate on the stiffness of CM, the used specimens only differ in the amount of stone aggregate and their shapes. Although the stone aggregates are assumed to be of cylindrical shapes (plane conditions), the compressive stiffness of CM works well with the mixture rule.
https://www.jcepm.com/article_54011_871fb6bf22ec71278230000a00c4c90a.pdf
2018-01-01
12
22
10.22115/cepm.2018.54011
Mechanical properties
stiffness
2-D Finite element analysis (FEA)
Concrete Mix
Mofid
Mahdi
mmahdi@kfu.edu.sa
1
Ph.D., Department of Mechanical Engineering, College of Engineering, King Faisal University, Al Hofoof, KSA
AUTHOR
Iqbal
Marie
iqbal@hu.edu.jo
2
M.Sc, Department of Civil Engineering, Faculty of Engineering, Hashemite University, Zarqa, Jordan
LEAD_AUTHOR
[1] Persson B. A comparison between mechanical properties of self-compacting concrete and the corresponding properties of normal concrete. Cem Concr Res 2001;31:193–8. doi:10.1016/S0008-8846(00)00497-X.
1
[2] Bahari A, Berenjian J, Sadeghi-Nik A. Modification of Portland Cement with Nano SiC. Proc Natl Acad Sci India Sect A Phys Sci 2016;86:323–31. doi:10.1007/s40010-015-0244-y.
2
[3] Sadeghi-Nik A, Berenjian J, Bahari A, Safaei AS, Dehestani M. Modification of microstructure and mechanical properties of cement by nanoparticles through a sustainable development approach. Constr Build Mater 2017;155:880–91. doi:10.1016/j.conbuildmat.2017.08.107.
3
[4] Bahari A, Sadeghi Nik A, Roodbari M, Mirshafiei E, Amiri B. Effect of Silicon Carbide Nano Dispersion on the Mechanical and Nano Structural Properties of Cement. Natl Acad Sci Lett 2015;38:361–4. doi:10.1007/s40009-014-0316-6.
4
[5] Wu K-R, Chen B, Yao W, Zhang D. Effect of coarse aggregate type on mechanical properties of high-performance concrete. Cem Concr Res 2001;31:1421–5. doi:10.1016/S0008-8846(01)00588-9.
5
[6] Rao GA, Prasad BKR. Influence of the roughness of aggregate surface on the interface bond strength. Cem Concr Res 2002;32:253–7. doi:10.1016/S0008-8846(01)00668-8.
6
[7] González-Peña R, Martı́-López L, Cibrián-Ortiz de Anda RM, Molina-Jiménez T, Piqueres-Ayela C. Measurement of Young’s modulus of cementitious materials using an electro-optic holographic technique. Opt Lasers Eng 2001;36:527–35. doi:10.1016/S0143-8166(01)00080-X.
7
[8] Grote DL, Park SW, Zhou M. Dynamic behavior of concrete at high strain rates and pressures: I. experimental characterization. Int J Impact Eng 2001;25:869–86. doi:10.1016/S0734-743X(01)00020-3.
8
[9] Wong YL, Lam L, Poon CS, Zhou FP. Properties of fly ash-modified cement mortar-aggregate interfaces. Cem Concr Res 1999;29:1905–13. doi:10.1016/S0008-8846(99)00189-1.
9
[10] Wang ZM, Kwan AKH, Chan HC. Mesoscopic study of concrete I: generation of random aggregate structure and finite element mesh. Comput Struct 1999;70:533–44. doi:10.1016/S0045-7949(98)00177-1.
10
[11] Willam K, Rhee I, Beylkin G. No Title. Meccanica 2001;36:131–50. doi:10.1023/A:1011905201001.
11
[12] Lai S, Serra M. Concrete strength prediction by means of neural network. Constr Build Mater 1997;11:93–8. doi:10.1016/S0950-0618(97)00007-X.
12
[13] Zhao X-H, Chen WF. Effective elastic moduli of concrete with interface layer. Comput Struct 1998;66:275–88. doi:10.1016/S0045-7949(97)00056-4.
13
[14] Hashin Z, Monteiro PJM. An inverse method to determine the elastic properties of the interphase between the aggregate and the cement paste. Cem Concr Res 2002;32:1291–300. doi:10.1016/S0008-8846(02)00792-5.
14
[15] Park SW, Xia Q, Zhou M. Dynamic behavior of concrete at high strain rates and pressures: II. numerical simulation. Int J Impact Eng 2001;25:887–910. doi:10.1016/S0734-743X(01)00021-5.
15
[16] Kwan AKH, Wang ZM, Chan HC. Mesoscopic study of concrete II: nonlinear finite element analysis. Comput Struct 1999;70:545–56. doi:10.1016/S0045-7949(98)00178-3.
16
[17] Li G, Zhao Y, Pang S-S. A three-layer built-in analytical modeling of concrete. Cem Concr Res 1998;28:1057–70. doi:10.1016/S0008-8846(98)00062-3.
17
[18] Khademi F, Jamal SM, Deshpande N, Londhe S. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. Int J Sustain Built Environ 2016;5:355–69. doi:10.1016/j.ijsbe.2016.09.003.
18
[19] Khademi F, Akbari M, Jamal SM, Nikoo M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng 2017;11:90–9. doi:10.1007/s11709-016-0363-9.
19
[20] Boresi AP, Chong K, Lee JD. Elasticity in engineering mechanics. Wiley; 2011.
20
[21] Callister WD, Rethwisch DG. Materials Science and Engineering An Introduction Library of Congress Cataloging-in-Publication Data 2003.
21
ORIGINAL_ARTICLE
Reliability-Based Investigation on Compressive Strength Characteristics of Structural-Sized Iroko (Meliceae Excelsa) and Mahogany (Khaya Ivorensis) Timber Column Found in Nigeria
This research work examined the reliability of the Nigerian grown Iroko and Mahogany timber species as column materials. The strength and physical properties of these timber species were determined to predict the suitability of the species as structural material. Forty lengths of timber species of 50mm x 50mm cross-section were purchased from timber market in Ilorin, Nigeria. The prevailing environmental conditions during the test were 31oC and 64% relative humidity. The properties tested included; air dry density, moisture content and compressive strength parallel to grain of forty test specimens each of lengths, 200, 400, 600 and 800mm done in accordance with the British Standard BS 373(1957). Mean air-dried moisture content for Iroko and Mahogany were 12.09 and 14.81% respectively. Mean density of Iroko and Mahogany were 500.8 and 830.1kg/m3 respectively. The derived continuous equations for design of Iroko column and Mahogany column are σ=〖37.552e〗^(-0.005λ) and σ=〖37.125e〗^(-0.007λ) respectively. The results of the reliability analysis show that Iroko and Mahogany timber species have reliability index of 0.64 and 0.65 for a service life of 50 years, assuming other serviceability conditions are met. This design procedure is distinct and more effective than the usual procedure of classifying compression members as short, intermediate and long using their slenderness ratios.
https://www.jcepm.com/article_54890_0cea11c86dfeea892fa925c54c308bb6.pdf
2018-01-01
23
37
10.22115/cepm.2018.105453.1002
Compressive strength
Iroko
Mahogany
regression analysis
Reliability
Abdullahi
Jimoh
aajimoh4real@yahoo.com
1
Civil Engineering Department, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
AUTHOR
Rauf
Rahmon
rorahmon2222@gmail.com
2
Civil Engineering Department, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
LEAD_AUTHOR
Sofiyyat
Ajide
saphiyyah12@gmail.com
3
Civil Engineering Department, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
AUTHOR
[1] Kermani A, Porteous J. Structural Timber Design to Eurocode 5 2007.
1
[2] Apu SS. Wood Structure and Construction Method for Low-cost Housing. Int. Semin. Build. Mater. Low-Cost Housing, Sept. 7, vol. 28, 2003.
2
[3] material RF-W handbook: wood as an engineering, 2010 undefined. Wood as a sustainable building material. FsUsdaGov n.d.
3
[4] Gere JM. Mechanics of Materials. Thomson Learning Inc 2004.
4
[5] Trahair NS. Flexural-torsional buckling of structures. Routledge; 2017.
5
[6] Ajamu SO. Optimal design of cement-lime plastered straw bale masonry under vertical load and thermal insulation for a residential building 2014.
6
[7] Nowak AS, Collins KR. Reliability of structures. CRC Press; 2012.
7
[8] Ozelton EC, Baird JA. Timber designers’ manual. John Wiley & Sons; 2008.
8
[9] Nowak AS. Survey of textbooks on reliability and structure. Rep Spec Proj Spons by Struct Eng Inst ASCE 2004.
9
[10] Ghasemi SH, Nowak AS. Target reliability for bridges with consideration of ultimate limit state. Eng Struct 2017;152:226–37. doi:10.1016/J.ENGSTRUCT.2017.09.012.
10
[11] Thelandersson S, Larsen HJ. Timber engineering. John Wiley & Sons; 2003.
11
[12] Aguwa JI, Sadiku S. Reliability studies on the Nigerian Ekki timber as bridge beam in bending under the ultimate limit state of loading. J Civ Eng Constr Technol 2011;2:253–9. doi:https://doi.org/10.5897/JCECT11.052.
12
[13] Aguwa JI. Reliability assessment of the Nigerian apa (afzelia bipindensis) timber bridge beam subjected to bending and deflection under the ultimate limit state of loading. Int J Eng Technol 2012;2:1076–88.
13
[14] Aguwa JI. Reliability studies on the nigerian timber as an orthotropic, elastic structural material. Unpubl Ph D Thesis Submitt to Post Grad Sch Fed Univ Technol Minna, Niger 2010.
14
[15] Ghasemi SH, Nowak AS. Reliability index for non-normal distributions of limit state functions. Struct Eng Mech 2017;62:365–72.
15
[16] Norge S. Timber structures—structural timber and glued laminated timber—determination of some physical and mechanical properties. Nor Stand NS-EN 2010;408.
16
[17] Standard B. BS 373: Methods of Testing Small Clear Specimens of Timber. Br Stand Institution, London 1957.
17
[18] Adedeji AA. Reliability-Based Probability Analysis for Predicting Failure of Earth Brick Wall in Compression 2008.
18
ORIGINAL_ARTICLE
Seismic Fragility Assessment of Local and Global Failures in Low-rise Non-ductile Existing RC Buildings: Empirical Shear-Axial Modelling vs. ASCE/SEI 41 Approach
The brittle behavior of older non-ductile reinforced concrete buildings such as shear-axial failure in columns can cause lateral instability or gravity collapse. Hence, the attempt is to assess the collapse potential through fragility curves. Current research focuses on fragility assessment of these buildings emphasizing on shear-axial failure using two well-established methods; empirical limit state material versus ASCE/SEI 41-13 recommendations. To this aim, two 2D reinforced concrete models (3 and 5-story) according to typical detail of existing buildings in Iran were modeled using two aforementioned modeling approaches and analyzed under monotonic analysis and incremental dynamic analysis (IDA). In the following, seismic fragility assessment were carried out by means of obtained results from IDA. The results of fragility curves showed that, collapse capacity of buildings modelled by ASCE/SEI 41-13 are more than empirical method and fewer cases can pass the level of safety probability of failure suggested by ASCE/SEI-41.
https://www.jcepm.com/article_59794_b27b6dc881ae33530cd8035a31ba3317.pdf
2018-01-01
38
57
10.22115/cepm.2018.114549.1008
Shear and axial failures
Local and global collapse
Non-ductile reinforced concrete buildings
Fragility curves
Mohammad Reza
Azadi Kakavand
mohammadreza.azadi86@gmail.com
1
Unit of Strength of Materials and Structural Analysis, Department of basic sciences in engineering sciences, University of Innsbruck, Innsbruck, Austria
LEAD_AUTHOR
Mohammad
Khanmohammadi
mkhan@ut.ac.ir
2
Faculty of Civil Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Allahvirdizadeh R, Mohammadi MA. Upgrading equivalent static method of seismic designs to performance-based procedure. Earthquakes Struct 2016;10:849–65. doi:10.12989/eas.2016.10.4.849.
1
[2] Allahvirdizadeh R, Khanmohammadi M, Marefat MS. Probabilistic comparative investigation on introduced performance-based seismic design and assessment criteria. Eng Struct 2017;151:206–20. doi:10.1016/j.engstruct.2017.08.029.
2
[3] Shafaei S, Ayazi A, Farahbod F. The effect of concrete panel thickness upon composite steel plate shear walls. J Constr Steel Res 2016;117:81–90. doi:10.1016/j.jcsr.2015.10.006.
3
[4] Rassouli B, Shafaei S, Ayazi A, Farahbod F. Experimental and numerical study on steel-concrete composite shear wall using light-weight concrete. J Constr Steel Res 2016;126:117–28. doi:10.1016/j.jcsr.2016.07.016.
4
[5] Ayazi A, Ahmadi H, Shafaei S. The effects of bolt spacing on composite shear wall behavior. World Acad Sci Eng Technol 2012;6:10–27.
5
[6] Shafaei S, Farahbod F, Ayazi A. Concrete Stiffened Steel Plate Shear Walls With an Unstiffened Opening. Structures 2017;12:40–53. doi:10.1016/j.istruc.2017.07.004.
6
[7] Shafaei S, Farahbod F, Ayazi A. The wall-frame and the steel-concrete interactions in composite shear walls. Struct Des Tall Spec Build 2018:e1476. doi:10.1002/tal.1476.
7
[8] Elwood KJ. Shake table tests and analytical studies on the gravity load collapse of reinforced concrete frames. 2004.
8
[9] Elwood KJ, Moehle JP. Evaluation of existing reinforced concrete columns. Proceedings, 2004.
9
[10] Elwood KJ. Modelling failures in existing reinforced concrete columns. Can J Civ Eng 2004;31:846–59. doi:10.1139/l04-040.
10
[11] Elwood KJ, Moehle JP. Dynamic Shear and Axial-Load Failure of Reinforced Concrete Columns. J Struct Eng 2008;134:1189–98. doi:10.1061/(ASCE)0733-9445(2008)134:7(1189).
11
[12] Kabeyasawa T, Kabeyasawa T, Kim Y. Progressive Collapse Simulation of Reinforced Concrete Buildings Using Column Models with Strength Deterioration after Yielding. Improv. Seism. Perform. Exist. Build. Other Struct., Reston, VA: American Society of Civil Engineers; 2009, p. 512–23. doi:10.1061/41084(364)47.
12
[13] Yavari S, Lin SH, Elwood KJ, Wu CL, Hwang SJ, Moehle JP. Study on collapse of flexure-shear-critical reinforced concrete frames. 14th World Conf. Earthq. Eng. Beijing, China, 2008.
13
[14] Yavari S, Elwood KJ, Wu C. Collapse of a nonductile concrete frame: Evaluation of analytical models. Earthq Eng Struct Dyn 2009;38:225–41. doi:10.1002/eqe.855.
14
[15] Mosalam KM, Talaat M, Park S. Modeling progressive collapse in reinforced concrete framed structures. Proc. 14th World Conf. Earthq. Eng., 2008, p. 12–7.
15
[16] Wu C, Kuo W-W, Yang Y-S, Hwang S-J, Elwood KJ, Loh C-H, et al. Collapse of a nonductile concrete frame: Shaking table tests. Earthq Eng Struct Dyn 2009;38:205–24. doi:10.1002/eqe.853.
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[17] Matamoros AB, Matchulat L, Woods C. Axial load failure of shear critical columns subjected to high levels of axial load. Proc. 14th World Conf. Earthq. Eng, Citeseer; 2008.
17
[18] Nakamura T, Yoshimura M. Gravity Load Collapse of Reinforced Concrete Columns with Brittle Failure Modes. J Asian Archit Build Eng 2002;1:21–7. doi:10.3130/jaabe.1.21.
18
[19] Nakamura T, Yoshimura M. Simulation of Old Reinforced Concrete Column Collapse by Pseudo-dynamic Test Method. World Conf. Earthq. Eng., vol. 12, 2012, p. 1–10.
19
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[21] Mostafaei H, Vecchio FJ. Uniaxial shear-flexure model for reinforced concrete elements. J Struct Eng 2008;134:1538–47.
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[22] Mostafaei H, Vecchio FJ, Kabeyasawa T. Deformation capacity of reinforced concrete columns. ACI Struct J 2009;106:187.
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[23] Murray JA, Sasani M. Evaluating System-Level Collapse Resistance of Non-Ductile RC Frames Structures. Proc. 10th Natl. Conf. Earthq. Eng. Earthq. Eng. Res. Institute, Anchorage, AK, 2014.
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[25] Sasani M. Shear Strength and Deformation Capacity Models for RC Columns. 13th World Conf. Earthq. Eng. Vancouver Canada, Pap., 2004.
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[26] Kyriakides N, Sohaib A, Pilakoutas K, Neocleous K, Chrysostomou C, Tantele E, et al. Evaluation of Seismic Demand for Substandard Reinforced Concrete Structures. Open Constr Build Technol J 2018;12:9–33. doi:10.2174/1874836801812010009.
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[27] Allahvirdizadeh R, Gholipour Y. Reliability evaluation of predicted structural performances using nonlinear static analysis. Bull Earthq Eng 2017;15:2129–48. doi:10.1007/s10518-016-0062-x.
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[32] Kyriakides NC, Pantazopoulou SJ. Collapse Fragility Curves for RC Buildings Exhibiting Brittle Failure Modes. J Struct Eng 2018;144:04017207. doi:10.1061/(ASCE)ST.1943-541X.0001920.
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39
ORIGINAL_ARTICLE
The Effect of Diagonal Stiffeners on the Behaviour of Stiffened Steel Plate Shear Wall
In the current study, the nonlinear behavior of the stiffened steel plate shear wall with diagonal stiffeners is numerically studied. After nonlinear pushover analysis, the finite element modeling results are compared with un-stiffened and stiffened steel plate shear walls, with the horizontal and vertical stiffeners. First, a finite element model of steel plate shear wall is developed and validated by using Abaqus software. After assuring the behavior of the boundary elements (beams and columns) and the infill steel plate, the finite element models of the steel shear walls are developed and analyzed using nonlinear pushover method. Steel plate shear wall models are designed according to AISC 341-10 Seismic Provisions. Finally, the obtained results and the behavior of finite element models are compared with each other. The important seismic parameters (initial elastic stiffness, ultimate shear strength, and ductility) are calculated and percentage of changes are discussed. Based on the results, the performance of steel plate shear walls with diagonal stiffeners enhances as compared with unstiffened steel plate shear walls.
https://www.jcepm.com/article_59795_bbe763272043f0f114e6578d008a19c2.pdf
2018-01-01
58
67
10.22115/cepm.2018.112951.1007
steel plate shear wall
Stiffened steel plate shear walls
Finite element method
nonlinear pushover analysis
Behtash
Amiri
amiri.behtash@gmail.com
1
Young Researchers Club, Roudehen Branch, Islamic Azad University, Roudehen, Iran
LEAD_AUTHOR
Hamed
AghaRezaei
hamed.agharezaei2018@yahoo.com
2
Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
AUTHOR
Reza
Esmaeilabadi
esmaeilabadi@riau.ac.ir
3
Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
AUTHOR
[1] Sabelli R, Bruneau M. Design guide 20: steel plate shear walls, American Institute of Steel Construction. Chicago, IL, USA 2007.
1
[2] Association CS. Limit states design of steel structures. Canadian Standards Association; 2001.
2
[3] Astaneh-Asl A. Seismic behavior and design of steel shear walls 2001.
3
[4] AISC A. AISC 341-10, Seismic Provisions for Structural Steel Buildings. Chicago, Am Inst Steel Constr 2010.
4
[5] Shafaei S, Ayazi A, Farahbod F. The effect of concrete panel thickness upon composite steel plate shear walls. J Constr Steel Res 2016;117:81–90. doi:10.1016/j.jcsr.2015.10.006.
5
[6] Rassouli B, Shafaei S, Ayazi A, Farahbod F. Experimental and numerical study on steel-concrete composite shear wall using light-weight concrete. J Constr Steel Res 2016;126:117–28. doi:10.1016/j.jcsr.2016.07.016.
6
[7] Ayazi A, Ahmadi H, Shafaei S. The effects of bolt spacing on composite shear wall behavior. World Acad Sci Eng Technol 2012;6:10–27.
7
[8] Shafaei S, Farahbod F, Ayazi A. Concrete Stiffened Steel Plate Shear Walls With an Unstiffened Opening. Structures 2017;12:40–53. doi:10.1016/j.istruc.2017.07.004.
8
[9] Sabouri-Ghomi S, Ventura CE, Kharrazi MH. Shear Analysis and Design of Ductile Steel Plate Walls. J Struct Eng 2005;131:878–89. doi:10.1061/(ASCE)0733-9445(2005)131:6(878).
9
[10] Soheil S, Farhang F, Amir A. The wall–frame and the steel–concrete interactions in composite shear walls. Struct Des Tall Spec Build 2018;0:e1476. doi:10.1002/tal.1476.
10
[11] Hosseinzadeh SAA, Tehranizadeh M. Introduction of stiffened large rectangular openings in steel plate shear walls. J Constr Steel Res 2012;77:180–92. doi:https://doi.org/10.1016/j.jcsr.2012.05.010.
11
[12] Berman JW, Bruneau M. Experimental investigation of light-gauge steel plate shear walls for the seismic retrofit of buildings 2003.
12
[13] Hibbitt, Karlsson, Sorensen. ABAQUS/Explicit: user’s manual. vol. 1. Hibbitt, Karlsson and Sorenson Incorporated; 2001.
13
[14] SHAFAEI S, RASSOULI B, AYAZI A, FARAHBOD F. NONLINEAR BEHAVIOR OF CONCRETE STIFFENED STEEL PLATE SHEAR WALL n.d.
14
[15] Lubell AS, Prion HGL, Ventura CE, Rezai M. Unstiffened Steel Plate Shear Wall Performance under Cyclic Loading. J Struct Eng 2000;126:453–60. doi:10.1061/(ASCE)0733-9445(2000)126:4(453).
15
ORIGINAL_ARTICLE
Settlement Modelling of Raft Footing Founded on Oferekpe/Abakaliki Shale in South East Region of Nigeria
In engineering practice, settlement of foundations are experimentally determined or numerically modelled based on conventional saturated soil mechanics principles. The study area, Oferekpe in Abakaliki LGA of Ebonyi State, South Eastern Region of Nigeria is characterised with sedimentary formations highly susceptible to compression under applied load. The study was aimed at evaluating raft footing settlement by both analytical and numerical modelling methods and determine the effect of raft thickness on settlement. Standard penetration test (SPT) data was used to correlate soil properties that were used together with laboratory results to obtain the input parameters used for the prediction of settlement. Four footing embedment depths of 1.5, 3.0, 4.5 and 6.0 m with applied foundation pressures of 50, 100, 200, 300, 400 and 500kN/m2 were considered using a raft footing dimension of 20 x 20 m2 at varying thickness of 0.5, 0.75 and 1.0 m. The numerical modelling finite element application package used was Plaxis 3D. For applied pressure of 100 kN/m2 and at footing embedment depths of 1.5, 3.0, 4.5 and 6.0 m, settlement values of (21.89, 11.51, 9.04 and 6.52), (19.70, 8.60, 6.41 and 4.39), (25.62, 14.88, 12.05 and 9.27) and (25.20, 11.59, 5.57 and 2.58) were respectively predicted by the elastic, semi-empirical, empirical and finite element methods. The elastic method of predicting foundation settlement proposed by Steinbrenner yielded a very close range results generally to those predicted by finite element method. It was generally observed that thickness of raft footing has no significant effect on the predicted settlement.
https://www.jcepm.com/article_60119_0ac03ddb65289c1a44c36a42fdde3ec7.pdf
2018-01-01
68
82
10.22115/cepm.2018.116754.1009
Raft foundation
Settlement prediction
Numerical modelling
Standard penetration test
Plaxis 3D
Salahudeen
Bunyamin
muazbj@gmail.com
1
Ph.D., Samaru College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University, Zaria, Nigeria
LEAD_AUTHOR
Saideh
Aghayan
saideh.aghayan@gmail.com
2
Ph.D., Candidate, Department of Mining and Metallurgical Engineering, Amirkabir University, Tehran, Iran
AUTHOR
[1] Klemencic R, McFarlane I, Hawkins N, Nikolaou S. NEHRP Seismic Design Technical Brief No. 7-Seismic Design of Reinforced Concrete Mat Foundations: A Guide for Practicing Engineers. 2012.
1
[2] Hussein H. Effects of flexural rigidity and soil modulus on the linear static analysis of raft foundations. J Babylon Univ Pure Appl Sci 2011;19.
2
[3] Chaudhary MTA. FEM modelling of a large piled raft for settlement control in weak rock. Eng Struct 2007;29:2901–7. doi:https://doi.org/10.1016/j.engstruct.2007.02.001.
3
[4] Ornek M, Demir A, Laman M, Yildiz A. Numerical analysis of circular footings on natural clay stabilized with a granular fill. Acta Geotech Slov 2012;1:61–75.
4
[5] Salahudeen AB, Eberemu AO, Ijimdiya TS, Osinubi KJ. Prediction of bearing capacity and settlement of foundations in the south-east of Nigeria, Book of Proceedings. Mater. Sci. Technol. Soc. Niger. Kaduna State Chapter Conf. July16, NARICT, Zaria, 2016.
5
[6] Salahudeen AB, Eberemu AO, Ijimdiya TS, Osinubi KJ. Empirical and numerical prediction of settlement and bearing capacity of foundations from SPT data in North-West region of Nigeria. Niger J Eng 2017;23 No.2:31–41.
6
[7] Johnson K, Christensen M, Karunasena NSW. Simulating the response of shallow foundations using finite element modelling 2003.
7
[8] Salahudeen AB, Ijimdiya TS, Eberemu AO, Osinubi KJ. Prediction of bearing capacity and settlement of foundations using standard penetration data in the South-South geo-political zone of Nigeria, Book of Proceedings. Int. Conf. Constr. Summit, Niger. Build. Road Res. Inst., 2016.
8
[9] Al-Jabban MJW. Estimation of standard penetration test (SPT) of Hilla city-Iraq by using GPS coordination. Jordan J Civ Eng 2013;7:133–45.
9
[10] Ola SA. Tropical soils of Nigeria in engineering practice 1983.
10
[11] Sadeeq JA, Salahudeen AB. STRENGTH CHARACTERIZATION OF FOUNDATION SOILS AT FEDERAL UNIVERSITY LOKOJA BASED ON STANDARD PENETRATION TESTS DATA. Niger J Technol 2017;36:671–6. doi:http://dx.doi.org/10.4314/njt.v36i3.2.
11
[12] Obasi AI, Okoro AU, Nweke OM, Chukwu A. Lithofacies and paleo depositional environment of the rocks of Nkpuma-Akpatakpa, Izzi, Southeast Nigeria. African J Environ Sci Technol 2013;7:967–75.
12
[13] Salahudeen AB, Aghayan S. Settlement Modelling of Raft Footing Founded on Oferekpe/Abakaliki Shale in South East Region of Nigeria. J Comput Eng Phys Model 2018;1:68–82.
13
[14] Terzaghi K, Peck RB, Mesri G. Soil mechanics in engineering practice. John Wiley & Sons; 1996.
14
[15] Schultze E, Sherif G. Prediction of settlements from evaluated settlement observations for sand. Proc. Eighth Int. Conf. Soil Mech. Found. Eng., vol. 1, 1973, p. 225–30.
15
[16] Das BM. Elastic settlement of shallow foundations on granular soil: a critical review 2015.
16
[17] Bolton Seed H, Tokimatsu K, Harder LF, Chung RM. Influence of SPT Procedures in Soil Liquefaction Resistance Evaluations. J Geotech Eng 1985;111:1425–45. doi:10.1061/(ASCE)0733-9410(1985)111:12(1425).
17
[18] Skempton AW. Standard penetration test procedures and the effects in sands of overburden pressure, relative density, particle size, ageing and overconsolidation. Géotechnique 1986;36:425–47. doi:10.1680/geot.1986.36.3.425.
18
[19] Brinkgreve RBJ. Tutorial Manual PLAXIS 3D Foundation. Delft Univ Technol Plaxis Bv, Netherl 2013.
19
[20] ASTM. Standard Test Method for Penetration Test and Split Barrel Sampling of Soils (D1586). West Conshohocken 2001. doi:http://dx.doi.org/10.4314/njt.v36i3.1.
20
[21] Bowles LE. Foundation analysis and design. McGraw-hill; 1996.
21
[22] Düzceer R. Observed and predicted settlement of shallow foundation. 2nd Int. Conf. New Dev. Soil Mech. Geotech. Eng., 2009.
22
[23] Shahin MA, Jaksa MB, Maier HR. Predicting the settlement of shallow foundations on cohesionless soils using back-propagation neural networks. Department of Civil and Environmental Engineering, University of Adelaide; 2000.
23
[24] Ahmed AY. Reliability analysis of settlement for shallow foundations in bridges 2013.
24
[25] Schmertmann JH, Hartman JP, Brown PR. Improved strain influence factor diagrams. J Geotech Geoenvironmental Eng 1978;104.
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[26] Burland JB, Burbridge MC. Settlement of foundations on sand and gravel. Inst. Civ. Eng. Proceedings, Pt 1, vol. 76, 1985.
26
[27] Mayne PW, Poulos HG. Approximate Displacement Influence Factors for Elastic Shallow Foundations. J Geotech Geoenvironmental Eng 1999;125:453–60. doi:10.1061/(ASCE)1090-0241(1999)125:6(453).
27
[28] Becker DE, Moore ID. Canadian foundation engineering manual 2006.
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[29] Raymond GP. Settlement of Foundations. Geotechnical Engineering 1997:113 – 126.
29
[30] SALAHUDEEN BA, SADEEQ JA. Evaluation of bearing capacity and settlement of foundations. Leonardo Electron J Pract Technol n.d.;15:93–114.
30
[31] Salahudeen AB, Sadeeq JA. INVESTIGATION OF SHALLOW FOUNDATION SOIL BEARING CAPACITY AND SETTLEMENT CHARACTERISTICS OF MINNA CITY CENTRE DEVELOPMENT SITE USING PLAXIS 2D SOFTWARE AND EMPIRICAL FORMULATIONS. Niger J Technol 2017;36:663–70.
31
ORIGINAL_ARTICLE
Flow and Pollutant Dispersion Model in a 2D Urban Street Canyons Using Computational Fluid Dynamics
A two-dimensional model is used to simulate temperature distribution, wind speed and pollutants dispersion within an isolated two-dimensional street canyon using SIMPLE algorithm in ANSYS Fluent version 16.2. The simulation is based on the Reynolds-averaged Navier–Stokes equations coupled with a series of standard, RNG and realizable k-ε turbulence models. Simulation domain consisted of a street canyon with two buildings enclosing a street with the aspect ratio of 1. The wind is assumed to be perpendicular to the direction of the street and the source of the pollution is assumed to be liner. The results showed that the RNG k-ε turbulence model is the most optimum model by comparing with the calculated data under different wind speed patterns and pollutant dispersion model. The improvement of turbulent viscosity term of the RNG k-ε turbulence model provides a more accurate and reliable numerical solution for the present study regarding to the pollution dispersion in a street canyon. The simulation results also showed that the dimensionless pollutant concentrations, P, is larger on the leeward side of the buildings and decrease exponentially from floor to top of the upstream buildings. Furthermore, the results showed that the pollutant concentrations on the leeward side of building are more than that on the windward side due to the pollutant transportation of vortex circulation.
https://www.jcepm.com/article_60258_1f7e270b6801c99adbdecd40213b6615.pdf
2018-01-01
83
93
10.22115/cepm.2018.122506.1014
Pollutant dispersion model
street canyon
Computational Fluid Dynamics
Reynolds-averaged Navier-Stokes equations
Zahra
Jandaghian
zahrajandaghian114@gmail.com
1
Ph.D., Research Assistant, Heat Island Group, Building, Civil and Environmental Engineering Department, Concordia University, Montreal, Quebec, Canada
LEAD_AUTHOR
[1] Fernando HJS, Lee SM, Anderson J, Princevac M, Pardyjak E, Grossman-Clarke S. Urban fluid mechanics: air circulation and contaminant dispersion in cities. Environ Fluid Mech 2001;1:107–64. doi:10.1023/A:1011504001479.
1
[2] Britter RE, Hanna SR. Flow and dispersion in urban areas. Annu Rev Fluid Mech 2003;35:469–96.
2
[3] Belcher SE. Mixing and transport in urban areas. Philos Trans R Soc A Math Phys Eng Sci 2005;363:2947–68. doi:10.1098/rsta.2005.1673.
3
[4] Oke TR. Street design and urban canopy layer climate. Energy Build 1988;11:103–13. doi:https://doi.org/10.1016/0378-7788(88)90026-6.
4
[5] Vardoulakis S, Fisher BEA, Pericleous K, Gonzalez-Flesca N. Modelling air quality in street canyons: a review. Atmos Environ 2003;37:155–82. doi:https://doi.org/10.1016/S1352-2310(02)00857-9.
5
[6] Ahmad K, Khare M, Chaudhry KK. Wind tunnel simulation studies on dispersion at urban street canyons and intersections—a review. J Wind Eng Ind Aerodyn 2005;93:697–717. doi:https://doi.org/10.1016/j.jweia.2005.04.002.
6
[7] Li X-X, Liu C-H, Leung DYC, Lam KM. Recent progress in CFD modelling of wind field and pollutant transport in street canyons. Atmos Environ 2006;40:5640–58. doi:https://doi.org/10.1016/j.atmosenv.2006.04.055.
7
[8] Sini J-F, Anquetin S, Mestayer PG. Pollutant dispersion and thermal effects in urban street canyons. Atmos Environ 1996;30:2659–77. doi:https://doi.org/10.1016/1352-2310(95)00321-5.
8
[9] Jandaghian Z, Touchaei AG, Akbari H. Sensitivity analysis of physical parameterizations in WRF for urban climate simulations and heat island mitigation in Montreal. Urban Clim 2017. doi:https://doi.org/10.1016/j.uclim.2017.10.004.
9
[10] Jandaghian Z, Akbari H. The Effect of Increasing Surface Albedo on Urban Climate and Air Quality: A Detailed Study for Sacramento, Houston, and Chicago. Climate 2018;6:19. doi:10.3390/cli6020019.
10
[11] Meroney RN, Pavageau M, Rafailidis S, Schatzmann M. Study of line source characteristics for 2-D physical modelling of pollutant dispersion in street canyons. J Wind Eng Ind Aerodyn 1996;62:37–56. doi:https://doi.org/10.1016/S0167-6105(96)00057-8.
11
[12] Kastner-Klein P, Berkowicz R, Britter R. The influence of street architecture on flow and dispersion in street canyons. Meteorol Atmos Phys 2004;87. doi:10.1007/s00703-003-0065-4.
12
[13] Kastner-Klein P, Rotach MW. Mean Flow and Turbulence Characteristics in an Urban Roughness Sublayer. Boundary-Layer Meteorol 2004;111:55–84. doi:10.1023/B:BOUN.0000010994.32240.b1.
13
[14] Ca VT, Asaeda T, Ito M, Armfield S. Characteristics of wind field in a street canyon. J Wind Eng Ind Aerodyn 1995;57:63–80. doi:https://doi.org/10.1016/0167-6105(94)00117-V.
14
[15] Uehara K, Murakami S, Oikawa S, Wakamatsu S. Wind tunnel experiments on how thermal stratification affects flow in and above urban street canyons. Atmos Environ 2000;34:1553–62. doi:https://doi.org/10.1016/S1352-2310(99)00410-0.
15
[16] Kim J-J, Baik J-J. Urban street-canyon flows with bottom heating. Atmos Environ 2001;35:3395–404. doi:https://doi.org/10.1016/S1352-2310(01)00135-2.
16
[17] Kim J-J, Baik J-J. Effects of inflow turbulence intensity on flow and pollutant dispersion in an urban street canyon. J Wind Eng Ind Aerodyn 2003;91:309–29. doi:https://doi.org/10.1016/S0167-6105(02)00395-1.
17
[18] Xie X, Liu C-H, Leung DYC, Leung MKH. Characteristics of air exchange in a street canyon with ground heating. Atmos Environ 2006;40:6396–409. doi:https://doi.org/10.1016/j.atmosenv.2006.05.050.
18
[19] Cheng H, Castro IP. Near Wall Flow over Urban-like Roughness. Boundary-Layer Meteorol 2002;104:229–59. doi:10.1023/A:1016060103448.
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[20] Coceal O, Thomas TG, Castro IP, Belcher SE. Mean Flow and Turbulence Statistics Over Groups of Urban-like Cubical Obstacles. Boundary-Layer Meteorol 2006;121:491–519. doi:10.1007/s10546-006-9076-2.
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[21] Hamlyn D, Hilderman T, Britter R. A simple network approach to modelling dispersion among large groups of obstacles. Atmos Environ 2007;41:5848–62. doi:https://doi.org/10.1016/j.atmosenv.2007.03.047.
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[22] Blocken B, Stathopoulos T, Saathoff P, Wang X. Numerical evaluation of pollutant dispersion in the built environment: Comparisons between models and experiments. J Wind Eng Ind Aerodyn 2008;96:1817–31. doi:https://doi.org/10.1016/j.jweia.2008.02.049.
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[23] Reynolds RT, Castro IP. Measurements in an urban-type boundary layer. Exp Fluids 2008;45:141–56. doi:10.1007/s00348-008-0470-z.
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[24] Buccolieri R, Sandberg M, Di Sabatino S. City breathability and its link to pollutant concentration distribution within urban-like geometries. Atmos Environ 2010;44:1894–903. doi:https://doi.org/10.1016/j.atmosenv.2010.02.022.
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[25] Kang Y-S, Baik J-J, Kim J-J. Further studies of flow and reactive pollutant dispersion in a street canyon with bottom heating. Atmos Environ 2008;42:4964–75. doi:https://doi.org/10.1016/j.atmosenv.2008.02.013.
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[28] Chan TL, Dong G, Leung CW, Cheung CS, Hung WT. Validation of a two-dimensional pollutant dispersion model in an isolated street canyon. Atmos Environ 2002;36:861–72. doi:https://doi.org/10.1016/S1352-2310(01)00490-3.
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[30] ANSYS Fluent V16.2. User Guide n.d.
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[31] Li X-X, Britter RE, Norford LK, Koh T-Y, Entekhabi D. Flow and Pollutant Transport in Urban Street Canyons of Different Aspect Ratios with Ground Heating: Large-Eddy Simulation. Boundary-Layer Meteorol 2012;142:289–304. doi:10.1007/s10546-011-9670-9.
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[32] Li X-X, Britter RE, Koh TY, Norford LK, Liu C-H, Entekhabi D, et al. Large-Eddy Simulation of Flow and Pollutant Transport in Urban Street Canyons with Ground Heating. Boundary-Layer Meteorol 2010;137:187–204. doi:10.1007/s10546-010-9534-8.
32