ORIGINAL_ARTICLE
An Interior-Constraint BEM for Regularization of Problems with Improper Boundary Conditions
A well-posed problem in the analysis of elastic bodies requires the definition of appropriate constraints of the boundary to prevent rigid body motion. However, one is sometimes presented with the problem of non-self-equilibrated tractions on an elastic body that will cause rigid body motion, while the boundary should remain unconstrained. One such case is the analysis of multi-particle dynamics where the solution is obtained in a quasi-static approach. In such cases, the motion of the particles is governed by the dynamic equilibrium while the contact forces between particles may be computed from elastostatic solutions. This paper presents two regularization methods of Interior-Constraint Boundary Element techniques for elastostatic analysis with improper boundary supports. In the proposed method rigid body modes are eliminated by imposing constraints on the interior of an elastic body. This is accomplished through simultaneously solving the governing Boundary Integral Equation and Somigliana’s Identity. The proposed method is examined through assessment and verification studies where it is demonstrated, that for all considered problems rigid body motion is successfully constrained with minimal effects on body deformations.
https://www.jsoftcivil.com/article_55277_c1743c6c5c26c52e63b41527da05da8d.pdf
2018-04-01
1
18
10.22115/scce.2018.108597.1036
Boundary element method
Rigid body motion constrains
Regularization
Approximate solutions
Grady
Mathews IV
gfm5121@psu.edu
1
Assistant Professor, Department of Civil Engineering, Penn State Harrisburg, Middletown, PA 17057, USA
AUTHOR
Dimitris
Rizos
rizos@engr.sc.edu
2
Associate Professor, Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
LEAD_AUTHOR
Robert
Mullen
rlm@sc.edu
3
Professor, Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
AUTHOR
[1] Cundall PA, Strack ODL. A discrete numerical model for granular assemblies. Geotechnique 1979;29:47–65.
1
[2] Poschel T, Schwager T. Computational Granular Dynamics: Models and Algorithms. 2005.
2
[3] Mathews IV GF. Developments for the advancement of the discrete element method. University of South Carolina, 2015.
3
[4] Vable M. Importance and use of rigid body mode in boundary element method. Int J Numer Methods Eng 1990;29:453–72. doi:10.1002/nme.1620290302.
4
[5] BLÁZQUEZ A, MANTIČ V, PARÍS F, CAÑAS J. ON THE REMOVAL OF RIGID BODY MOTIONS IN THE SOLUTION OF ELASTOSTATIC PROBLEMS BY DIRECT BEM. Int J Numer Methods Eng 1996;39:4021–38. doi:10.1002/(SICI)1097-0207(19961215)39:233.0.CO;2-Q.
5
[6] Vodička R, Mantič V, París F. Note on the removal of rigid body motions in the solution of elastostatic traction boundary value problems by SGBEM. Eng Anal Bound Elem 2006;30:790–8. doi:10.1016/j.enganabound.2006.04.002.
6
[7] Vodička R, Mantič V, París F. On the removal of the non-uniqueness in the solution of elastostatic problems by symmetric Galerkin BEM. Int J Numer Methods Eng 2006;66:1884–912. doi:10.1002/nme.1605.
7
[8] Fredholm I. Sur une classe d’équations fonctionnelles. Acta Math 1903;27:365–90. doi:10.1007/BF02421317.
8
[9] Asadollahi P, Tonon F. Coupling of BEM with a large displacement and rotation algorithm. Int J Numer Anal Methods Geomech 2011;35:749–60. doi:10.1002/nag.916.
9
[10] Rump SM. Inversion of extremely Ill-conditioned matrices in floating-point. Jpn J Ind Appl Math 2009;26:249–77. doi:10.1007/BF03186534.
10
[11] Lutz E, Ye W, Mukherjee S. Elimination of rigid body modes from discretized boundary integral equations. Int J Solids Struct 1998;35:4427–36. doi:10.1016/S0020-7683(97)00261-8.
11
[12] Sapountzakis EJ, Dikaros IC. Advanced 3D beam element of arbitrary composite cross section including generalized warping effects. Int J Numer Methods Eng 2015;102:44–78. doi:10.1002/nme.4849.
12
[13] Chen JT, Chen WC, Lin SR, Chen IL. Rigid body mode and spurious mode in the dual boundary element formulation for the Laplace problems. Comput Struct 2003;81:1395–404. doi:10.1016/S0045-7949(03)00013-0.
13
[14] Xiao Y-X, Zhang P, Shu S. An algebraic multigrid method with interpolation reproducing rigid body modes for semi-definite problems in two-dimensional linear elasticity. J Comput Appl Math 2007;200:637–52. doi:10.1016/j.cam.2006.01.021.
14
[15] Ko YY, Chen CH. Application of symmetric Galerkin boundary element method on elastostatic neumann problems. Int Assoc Comput Methods Adv Geomech 2008:146–53.
15
[16] Rizzo FJ. An integral equation approach to boundary value problems of classical elastostatics. Q Appl Math 1967;25:83–95. doi:10.1090/qam/99907.
16
[17] Wagdy M, Rashed YF. Boundary element analysis of multi-thickness shear-deformable slabs without sub-regions. Eng Anal Bound Elem 2014;43:86–94. doi:10.1016/j.enganabound.2014.03.011.
17
[18] Lamé G. Leçons sur la théorie mathématique de l’elasticité des corps solides par G. Lamé. Gauthier-Villars; 1852.
18
[19] Brebbia CA, Dominguez J. Boundary elements: an introductory course. WIT press; 1994.
19
[20] Cauchy AL. Sur un nouveau genre de calcul analogue au calcul infinitésimal. Oeuvres Complet d’Augustin Cauchy, Gauthier-Villars, Paris 1826.
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[21] Kelvin, Lord. Note on the integration of the equations of equilibrium of an elastic solid. Cambridge Dublin Math J 1848;3:87–9.
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[22] Kronecker L. Vorlesungen über die Theorie der Determinanten: Erste bis Einundzwanzigste Vorlesungen. vol. 2. BG Teubner; 1903.
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[23] Mineur H, Berthod-Zaborowski M, Henri B, Jean M. Techniques de calcul numérique: à l’usage des mathématiciens, astronomes, physiciens et ingénieurs. Dunod; 1952.
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[24] Hadamard J. Le problème de Cauchy et les équations aux derivées partielles linéaires hyperboliques 1932.
24
[25] Dassault. Abaqus 6.8 Program 2008.
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[26] Slaughter WS. Variational Methods. Linearized Theory Elast., Boston, MA: Birkhäuser Boston; 2002, p. 387–429. doi:10.1007/978-1-4612-0093-2_10.
26
ORIGINAL_ARTICLE
Comparative Analysis of Rigid Pavement Using Westergaard Method and Computer Program
Country’s economic, social and cultural development is mainly dependent on the performance of its highway structure. Selection of appropriate pavement type and related design method are vital for the improvement of pavement performance and its service life, and reduction in the initial and maintenance cost. The rigid pavement exposed to many distresses during its service life resulted due to the variation of traffic loading, material properties, and climatic conditions. The main objective of this project is to make a comparison between manual and computer design for rigid pavement structure under different loading, material properties, and temperature regimes. For manual design and computer design, “Westergaard Method” and “KENPAVE software” were used respectively. The stress analysis results revealed that edge stresses are higher as compared with interior and corner location, and stresses estimated at all locations with Westergaard method are significantly lower than stresses estimated with KENPAVE software. Results of sensitivity analysis showed that change in pavement thickness, material properties, and wheel load has a significant impact on developed stresses at different slab locations.
https://www.jsoftcivil.com/article_56038_a2bdbcff929859b0600dafa34bee72ee.pdf
2018-04-01
19
30
10.22115/scce.2018.110910.1040
Rigid pavement
Westergaard Method
Traffic Loading
Curling Stresses
Intisar
Al-Ghafri
07008082@uofn.edu.om
1
College of Engineering and Architecture, University of Nizwa, Initial Campus at Birkat-al-Mouz, 616, Nizwa, Oman
AUTHOR
Muhammad
Javid
ma.javid@hotmail.com
2
College of Engineering and Architecture, University of Nizwa, Initial Campus at Birkat-al-Mouz, 616, Nizwa, Oman
LEAD_AUTHOR
[1] S.P. Chandola. Transportation Engineering. AN ISO 9001: 2000 company. 2000.
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[2] Chakroborty P, Das A. Principles of transportation engineering. PHI Learning Pvt. Ltd.; 2007.
2
[3] Setyawan A, Zoorob SE, Hasan KE. Investigating and Comparing Traffic Induced and Restrained Temperature Stresses in a Conventional Rigid Pavement and Semi-Rigid Layers. Procedia Eng 2013;54:875–84. doi:10.1016/j.proeng.2013.03.080.
3
[4] Jackson N, Puccinelli J. effects of multiple freeze cycles and deep frost penetration on pavement performance and cost. FHWA-HRT-06-121 November; 2006.
4
[5] Khan MI, Qadeer MA, Harwalkar AB. Mechanistic Analysis of Rigid Pavement for Temperature Stresses Using Ansys. IOSR J Mech Civ Eng 2014;11:90–107.
5
[6] Zones IHW. HIGHWAY DESIGN MANUAL 2006.
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[7] Officials T. AASHTO Guide for Design of Pavement Structures. AASHTO; 1993.
7
[8] www.dot.state.fl.us n.d.
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[9] Medani TO, Ziedan AS, Hussein AG. Initial Cost Comparison of Rigid and Flexible Pavements Case Study: Khartoum State. Univ Khartoum Eng J 2016;4.
9
[10] Bartosova L. Stresses from Loading on Rigid Pavement. 2003.
10
[11] Belshe M, Mamlouk MS, Kaloush KE, Rodezno M. Temperature Gradient and Curling Stresses in Concrete Pavement with and without Open-Graded Friction Course. J Transp Eng 2011;137:723–9. doi:10.1061/(ASCE)TE.1943-5436.0000254.
11
ORIGINAL_ARTICLE
Profiled Composite Slab Strength Determination Method
The purpose of this article is to develop a new numerical approach for determining the strength capacity of a profiled composite slab (PCS) devoid of the current challenges of expensive and complex laboratory procedure required for establishing its longitudinal shear capacity. The new Failure Test Load (FTL) methodology is from a reliability-based evaluation of PCS load capacity design with longitudinal shear estimation under slope-intercept (m-k) method. The limit-state capacity development is through consideration of the experimental FTL value as the maximum material strength, and design load equivalent estimation using the shear capacity computation. This facilitates the complex strength verification of PDCS in a more simplified form that is capable of predicting FTL value, which will aid in determining the longitudinal shear of the profiled deck composite slab with ease. The developed strength determination effectively performs well in mimicking the probabilistic deck performance and composite slab strength determination. The strength test performance between the developed scheme and the experiment-based test results indicates high similarity, demonstrating the viability of the proposed strength determination methodology.
https://www.jsoftcivil.com/article_57404_95aec25e633b2f03f841d576a26f6075.pdf
2018-04-01
31
55
10.22115/scce.2018.102399.1030
Slope-intercept method
Reliability
Profiled composite slab
Longitudinal shear
First order reliability method
Strength test
Kachalla
Mohammed
engrkachalla@unimaid.edu.ng
1
Senior Lecturer, Department of Civil & Water Resources Engineering, University of Maiduguri, Maiduguri, Nigeria
LEAD_AUTHOR
Izian
Karim
izian_abd@upm.edu.my
2
Senior Lecturer, Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysia
AUTHOR
Farah
Aziz
farah@upm.edu.my
3
Associate Professor, Department of Civil Engineering, University Putra Malaysia, Malaysia
AUTHOR
Teik Hua
Law
lawteik@upm.edu.my
4
Associate Professor, Department of Civil Engineering, University Putra Malaysia, Malaysia
AUTHOR
[1] Karim IA, Mohammed K, Aziz NFAA, Hua LT. Comparative Safety Performance Evaluation of Profiled Deck Composite Slab from the Use of Slope-Intercept and Partial Shear Methods. World Acad Sci Eng Technol Int J Civil, Environ Struct Constr Archit Eng 2015;9:1047–53.
1
[2] Marčiukaitis G, Jonaitis B, Valivonis J. Analysis of deflections of composite slabs with profiled sheeting up to the ultimate moment. J Constr Steel Res 2006;62:820–30. doi:10.1016/j.jcsr.2005.11.022.
2
[3] Cifuentes H, Medina F. Experimental study on shear bond behavior of composite slabs according to Eurocode 4. J Constr Steel Res 2013;82:99–110. doi:10.1016/j.jcsr.2012.12.009.
3
[4] Mohammed BS. Structural behavior and m–k value of composite slab utilizing concrete containing crumb rubber. Constr Build Mater 2010;24:1214–21. doi:10.1016/j.conbuildmat.2009.12.018.
4
[5] Abdullah R, Samuel Easterling W. New evaluation and modeling procedure for horizontal shear bond in composite slabs. J Constr Steel Res 2009;65:891–9. doi:10.1016/j.jcsr.2008.10.009.
5
[6] Abdullah R, Hong Kueh AB, Ibrahim IS, Easterling WS. CHARACTERIZATION OF SHEAR BOND STRESS FOR DESIGN OF COMPOSITE SLABS USING AN IMPROVED PARTIAL SHEAR CONNECTION METHOD. J Civ Eng Manag 2015;21:720–32. doi:10.3846/13923730.2014.893919.
6
[7] An L. Load bearing capacity and behaviour of composite slabs with profiled steel sheet 1993.
7
[8] Crisinel M, Marimon F. A new simplified method for the design of composite slabs. J Constr Steel Res 2004;60:481–91. doi:10.1016/S0143-974X(03)00125-1.
8
[9] Degtyarev V V. Reliability-Based Evaluation of U.S. Design Provisions for Composite Steel Deck in Construction Stage. J Struct Eng 2012;138:308–17. doi:10.1061/(ASCE)ST.1943-541X.0000437.
9
[10] Johnson RP. Models for the Longitudinal Shear Resistance of Composite Slabs, and the Use of Non-Standard Test Data. Compos Constr Steel Concr V, Reston, VA: American Society of Civil Engineers; 2006, p. 157–65. doi:10.1061/40826(186)16.
10
[11] Johnson RP. Composite structures of steel and concrete: beams, slabs, columns, and frames for buildings. John Wiley & Sons; 2008.
11
[12] BEng SH, Park S. EN 1994-Eurocode 4: Design of composite steel and concrete structures n.d.
12
[13] Gholamhoseini A, Gilbert RI, Bradford MA, Chang ZT. Longitudinal shear stress and bond–slip relationships in composite concrete slabs. Eng Struct 2014;69:37–48. doi:10.1016/j.engstruct.2014.03.008.
13
[14] Marimuthu V, Seetharaman S, Arul Jayachandran S, Chellappan A, Bandyopadhyay TK, Dutta D. Experimental studies on composite deck slabs to determine the shear-bond characteristic values of the embossed profiled sheet. J Constr Steel Res 2007;63:791–803. doi:10.1016/j.jcsr.2006.07.009.
14
[15] Holmes N, Dunne K, O’Donnell J. Longitudinal shear resistance of composite slabs containing crumb rubber in concrete toppings. Constr Build Mater 2014;55:365–78. doi:10.1016/j.conbuildmat.2014.01.046.
15
[16] Hedaoo N, Gupta L, Ronghe G. Design of composite slabs with profiled steel decking: a comparison between experimental and analytical studies. Int J Adv Struct Eng 2012;4:1. doi:10.1186/2008-6695-3-1.
16
[17] EC4 E in D of composite steel and concrete structures. Part1.1: General rules and rules for building (PrEN 1994-1-1:2003) 2003.
17
[18] Okasha NM, Aichouni M. Proposed Structural Reliability-Based Approach for the Classification of Concrete Quality. J Mater Civ Eng 2015;27:04014169. doi:10.1061/(ASCE)MT.1943-5533.0001131.
18
[19] Robert EM. Structural reliability analysis and prediction. Baffins Lane, Chichester, West Sussex, Engl Wiley 1999.
19
[20] Adrzej SN, Anna MR, Ewa KS. Revised statistical resistance model for reinforced concrete structural component. ACI 2012;284:1–16.
20
[21] Ellingwood B, Galambos T V. Probability-based criteria for structural design. Struct Saf 1982;1:15–26. doi:10.1016/0167-4730(82)90012-1.
21
[22] Chen S. Load carrying capacity of composite slabs with various end constraints. J Constr Steel Res 2003;59:385–403. doi:10.1016/S0143-974X(02)00034-2.
22
[23] Abdinasir Y, Abdullah R, Mustaffa M. Modelling of shear bond with cohesive element and slenderness study of composite slabs. Proc Jt Conf 8th Asia Pacific Struct Eng Constr Conf 1st Int Conf Civ Eng Conf (ICCER), APSEC-ICCER 2012, 2012, p. 2–4.
23
[24] Honfi D, Mårtensson A, Thelandersson S. Reliability of beams according to Eurocodes in serviceability limit state. Eng Struct 2012;35:48–54. doi:10.1016/j.engstruct.2011.11.003.
24
[25] Schumacher A, Lääne A, Crisinel M. Development of a New Design Approach for Composite Slabs. Compos Constr Steel Concr IV, Reston, VA: American Society of Civil Engineers; 2002, p. 322–33. doi:10.1061/40616(281)28.
25
[26] Rana MM, Uy B, Mirza O. Experimental and numerical study of end anchorage in composite slabs. J Constr Steel Res 2015;115:372–86. doi:10.1016/j.jcsr.2015.08.039.
26
[27] Ong KCG, Mansurt MA. Shear-bond capacity of composite slabs made with profiled sheeting. Int J Cem Compos Light Concr 1986;8:231–7. doi:10.1016/0262-5075(86)90050-3.
27
ORIGINAL_ARTICLE
Stream Flow Forecasting Using Least Square Support Vector Regression
Accurate forecasting of streamflow for different lead-times is useful in the design of almost all hydraulic structures. The Support Vector Machines (SVMs) use a hypothetical space of linear functions in a kernel-induced higher dimensional feature space and are trained with a learning algorithm from optimization theory. The support vector regression attempts to fit a curve on data points such that the points lie between two marginal hyperplanes which will minimize the error. The current paper presents least square support vector regression (LS-SVR) to predict one day ahead stream flow using past values of the rainfall and river flow at three stations in India, namely Nighoje and Budhwad in Krishna river basin and Mandaleshwar in Narmada river basin. The relevant inputs are finalized on the basis of three techniques namely autocorrelation, Cross-correlation and trial and error. The forecasting model results are reasonable as can be seen from a low value of Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and high values of Coefficient of Efficiency (CE) accompanied by balanced scatter plots and hydrographs.
https://www.jsoftcivil.com/article_54124_46d602bd8e2d70614aaed6085ba6196f.pdf
2018-04-01
56
88
10.22115/scce.2017.96717.1024
Stream flow forecasting
Support vector regression
Kernel function
Shreenivas
Londhe
shreenivas.londhe@viit.ac.in
1
Professor, Vishwakarma Institute of Information Technology, Pune, India
LEAD_AUTHOR
Seema
Gavraskar
seemagavraskar@gmail.com
2
PG Student, Vishwakarma Institute of Information Technology, Pune, India
AUTHOR
[1] Shalamu A. Monthly and seasonal streamflow forecasting in the Rio Grande Basin. Ph.D. thesis, New Mixico State University, 2009.
1
[2] Bhatnagar A. Hydrologic Time Series Analysis using Support Vector Regression, M. Tech Thesis-2009, Indian Inst Technol Bombay 2009.
2
[3] Solomatine DP, Wagener T. Hydrological modeling 2011.
3
[4] Shrestha RR, Nestmann F. Physically Based and Data-Driven Models and Propagation of Input Uncertainties in River Flood Prediction. J Hydrol Eng 2009;14:1309–19. doi:10.1061/(ASCE)HE.1943-5584.0000123.
4
[5] http://www.cwc.gov.in/main/HP n.d.
5
[6] www.mahap.org n.d.
6
[7] Mahjoobi J, Adeli Mosabbeb E. Prediction of significant wave height using regressive support vector machines. Ocean Eng 2009;36:339–47. doi:10.1016/j.oceaneng.2009.01.001.
7
[8] Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Networks 1999;10:988–99. doi:10.1109/72.788640.
8
[9] Dibike YB, Velickov S, Solomatine D, Abbott MB. Model Induction with Support Vector Machines: Introduction and Applications. J Comput Civ Eng 2001;15:208–16. doi:10.1061/(ASCE)0887-3801(2001)15:3(208).
9
[10] Wu CL, Chau KW, Li YS. River stage prediction based on a distributed support vector regression. J Hydrol 2008;358:96–111. doi:10.1016/j.jhydrol.2008.05.028.
10
[11] Suykens JAK, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Process Lett 1999;9:293–300. doi:10.1023/A:1018628609742.
11
[12] Rajasekaran S, Gayathri S, Lee T-L. Support vector regression methodology for storm surge predictions. Ocean Eng 2008;35:1578–87. doi:10.1016/j.oceaneng.2008.08.004.
12
[13] Dibike YB, Velickov S, Solomatine D. Support vector machines: Review and applications in civil engineering. Proc. 2nd Jt. Work. Appl. AI Civ. Eng., Citeseer; 2000, p. 215–8.
13
[14] Bray M, Han D. Identification of support vector machines for runoff modelling. J Hydroinformatics 2004;6:265 LP-280.
14
[15] Asefa T, Kemblowski M, McKee M, Khalil A. Multi-time scale stream flow predictions: The support vector machines approach. J Hydrol 2006;318:7–16. doi:10.1016/j.jhydrol.2005.06.001.
15
[16] LIN J-Y, CHENG C-T, CHAU K-W. Using support vector machines for long-term discharge prediction. Hydrol Sci J 2006;51:599–612. doi:10.1623/hysj.51.4.599.
16
[17] Yu P-S, Chen S-T, Chang I-F. Support vector regression for real-time flood stage forecasting. J Hydrol 2006;328:704–16. doi:10.1016/j.jhydrol.2006.01.021.
17
[18] Behzad M, Asghari K, Eazi M, Palhang M. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst Appl 2009;36:7624–9. doi:10.1016/j.eswa.2008.09.053.
18
[19] Noori R, Karbassi AR, Moghaddamnia A, Han D, Zokaei-Ashtiani MH, Farokhnia A, et al. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. J Hydrol 2011;401:177–89. doi:10.1016/j.jhydrol.2011.02.021.
19
[20] Kisi O. Modeling discharge-suspended sediment relationship using least square support vector machine. J Hydrol 2012;456–457:110–20. doi:10.1016/j.jhydrol.2012.06.019.
20
[21] Bhagwat PP, Maity R. Hydroclimatic streamflow prediction using Least Square-Support Vector Regression. ISH J Hydraul Eng 2013;19:320–8. doi:10.1080/09715010.2013.819705.
21
[22] Sahraei S, Andalani SZ, Zakermoshfegh M, Sisakht BN, Talebbeydokhti N, Moradkhani H. Daily discharge forecasting using least square support vector regression and regression tree. Sci Iran Trans A, Civ Eng 2015;22:410.
22
[23] Kalteh AM. Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting. Water Resour Manag 2015;29:1283–93. doi:10.1007/s11269-014-0873-y.
23
[24] Kalteh AM. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Comput Geosci 2013;54:1–8. doi:10.1016/j.cageo.2012.11.015.
24
[25] Kalteh AM. Improving Forecasting Accuracy of Streamflow Time Series Using Least Squares Support Vector Machine Coupled with Data-Preprocessing Techniques. Water Resour Manag 2016;30:747–66. doi:10.1007/s11269-015-1188-3.
25
[26] Kisi O. Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering. Water Resour Manag 2015;29:5109–27. doi:10.1007/s11269-015-1107-7.
26
[27] Zamani A, Solomatine D, Azimian A, Heemink A. Learning from data for wind–wave forecasting. Ocean Eng 2008;35:953–62. doi:10.1016/j.oceaneng.2008.03.007.
27
[28] Londhe S., N., Dixit P.,R. CSB. Forecasting Ocean Waves using Support Vector Regression. Proc. of 18thIAHR-APD2012- 2012, Jeju, South Korea, n.d., p. 38–41.
28
[29] Legates DR, McCabe GJ. Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation. Water Resour Res 1999;35:233–41. doi:10.1029/1998WR900018.
29
[30] Dawson CW, Wilby RL. Hydrological modelling using artificial neural networks. Prog Phys Geogr 2001;25:80–108. doi:10.1177/030913330102500104.
30
[31] Londhe SN, Panchang V. One-Day Wave Forecasts Based on Artificial Neural Networks. J Atmos Ocean Technol 2006;23:1593–603. doi:10.1175/JTECH1932.1.
31
ORIGINAL_ARTICLE
Reliability Analysis of Structures Using Modified FA-PSO Algorithm
Designing buildings with a very high safety factor is one of the main purposes of a civil engineer. Since in the structural design process, there are several no-confidence; we cannot achieve a perfect safe design. In these cases, we face amount of the probability of failure. So the theory of reliability used to assess the uncertainty. This theoretical for expression the safety of a system uses the reliability index, so it can be said that the calculation of reliability index is an important part of the theory. By the theory of structural reliability, uncertainties arising from the nature of the statistical parameters can be written mathematical equations and considerations of safety and performance of the structure into the design process. Since classical methods are not capable of solving complex functions, metaheuristic algorithms used. In fact, a metaheuristic algorithm is a set of concepts, which significantly able to solve many complex issues, which they can reach an optimal solution in a short time. In this paper, the particle swarm algorithm combined with Firefly and to assess the reliability theory has been used. Reliability index is calculated by searching the shortest distance between the origin and the closed point of Limit State Surface in the Standard normalized space.Mathematical and engineering studies on the issues indicated; Hybrid Firefly and particle swarm algorithm are with great accuracy and speed.
https://www.jsoftcivil.com/article_56144_ed0c803b1f5aaf5afe8f8a56cf5181b9.pdf
2018-04-01
89
101
10.22115/scce.2018.114034.1045
Reliability Index
Engineering Problems
Limit State Function
Modified FA-PSO algorithm
Hamidreza
Shahmoradi Qomi
phd.shahmoardi@gmail.com
1
Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
Pavlo
Voitenko
pzv0011@auburn.edu
2
Ph.D. Candidate, Graduate Research Assistant, Department of Civil Engineering, Auburn University, Auburn, United States
LEAD_AUTHOR
Majid
Taheri
majidtaheri@semnan.ac.ir
3
Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
AUTHOR
[1] Santosh TV, Saraf RK, Ghosh AK, Kushwaha HS. Optimum step length selection rule in modified HL–RF method for structural reliability. Int J Press Vessel Pip 2006;83:742–8. doi:10.1016/j.ijpvp.2006.07.004.
1
[2] Melchers RE. Structural Reliability Analysis and Prediction. Ellis Horwood, John Wiley; 1987.
2
[3] Durbin R, Sulston J, Fraser S. Areas of Expertise. Biochemistry 1987;6:1990.
3
[4] Forsell C. Economy and construction. Sunt Förnuft 1924;4:74–7.
4
[5] Freudenthal AM. The safety of structures. Trans Am Soc Civ Eng 1947;112:125–59.
5
[6] Johnson AI. Strength, safety and economical dimensions of structures. Petterson; 1953.
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[10] Cheng J. Hybrid genetic algorithms for structural reliability analysis. Comput Struct 2007;85:1524–33. doi:10.1016/j.compstruc.2007.01.018.
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[11] Kirkegaard PH, Sorensen JD, Čizmar D, Rajčić V. System reliability of timber structures with ductile behaviour. Eng Struct 2011;33:3093–8. doi:10.1016/j.engstruct.2011.03.011.
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[12] Jahani E, Shayanfar MA, Barkhordari MA. A new adaptive importance sampling Monte Carlo method for structural reliability. KSCE J Civ Eng 2013;17:210–5. doi:10.1007/s12205-013-1779-6.
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[13] Eamon CD, Jensen E. Reliability Analysis of RC Beams Exposed to Fire. J Struct Eng 2013;139:212–20. doi:10.1061/(ASCE)ST.1943-541X.0000614.
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[15] Chojaczyk AA, Teixeira AP, Neves LC, Cardoso JB, Guedes Soares C. Review and application of Artificial Neural Networks models in reliability analysis of steel structures. Struct Saf 2015;52:78–89. doi:10.1016/j.strusafe.2014.09.002.
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[16] Ghasemi SH, Nowak AS. Reliability index for non-normal distributions of limit state functions. Struct Eng Mech 2017;62:365–72.
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[17] Ghasemi SH, Nowak AS. Mean maximum values of non-normal distributions for different time periods. Int J Reliab Saf 2016;10:99. doi:10.1504/IJRS.2016.078381.
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[18] 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.
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[19] Yanaka M, Hooman Ghasemi S, Nowak AS. Reliability-based and life-cycle cost-oriented design recommendations for prestressed concrete bridge girders. Struct Concr 2016;17:836–47. doi:10.1002/suco.201500197.
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22
ORIGINAL_ARTICLE
Modeling of Compressive Strength Characteristics of Structural-sized Afara (Terminalia superba) and Babo (Isoberlinia doka) Timber Columns Using Constant Failure Rate (CFR) Model of Reliability
This paper investigated the reliability of the Structural-sized Afara and Babo timber species as column materials. The work centers on the compressive strength characteristics of Nigerian Afara (Terminalia superba) and Babo (Isoberlinia doka) timber column of nominal lengths 200, 400, 600 and 800 mm and a nominal width and thickness of 50 mm by 50 mm. The steps involved collection and conditioning of Afara and Babo timber species, preparation of test specimens, determination of physical properties such as moisture content and density, determination of compressive strengths using varying heights of 200, 400, 600 and 800 mm and derivation of continuous column design equations. Forty test samples were used in all the tests carried out. Afara and Babo have an average density of 509.80and 849.67 kg/m3 respectively. The moisture content of both species less than the maximum recommended value of 20 % and the average strength at yield of Afara and Babo are 19.99 and 30.96 N/mm2. The derived continuous equations for the design of Afara column and Babo column are σ=16.992e0.0039λ and σ=32.031e-0.001λ respectively. The results of the reliability analysis show that Afara and Babo timber species have reliability index of 0.63 and 0.64 respectivelyfor 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 classification of compression members as short, intermediate and long.The paper therefore recommends the adoption of these equations for the design of compression members from these timber species in Nigeria.
https://www.jsoftcivil.com/article_57405_304dc9373b71321723021483e78a4eed.pdf
2018-04-01
102
115
10.22115/scce.2018.105504.1033
Afara
Babo
Compressive strength
regression analysis
Reliability
Alao
Jimoh
aajimoh4real@yahoo.com
1
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
AUTHOR
Rauf
Rahmon
rorahmon2222@gmail.com
2
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
LEAD_AUTHOR
Khalid
Ibrahim
awadrada@gmail.com
3
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
AUTHOR
[1] Aguwa JI, Chukwu PC, Auta SM. Characterization and Grading of South Eastern Nigeria grown Irvingia gabonensis Timber in Accordance with BS 5268. USEP J Res Inf Civ Eng 2015;12:720–31.
1
[2] A. A. Jimoh, R. O. Rahmon OYB and OLT. Characterization and Classification of Ayunre (Albizia zygia) Timber Specie grown in Kwara State Nigeria in accordance to bS 5268 nad NCP 2. Epistemics Sci Eng Technol 2017;7:549–57.
2
[3] Apu SS. Wood Structure and Construction Method for Low-cost Housing. Int. Semin. Build. Mater. Low-cost Housing, Indones., 2003, p. 7–28.
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[4] material RF-W handbook: wood as an engineering, 2010 undefined. Wood as a sustainable building material. FsUsdaGov n.d.
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[6] Aguwa JI. Reliability studies on the nigerian timber as an orthotropic, elastic structural material. 2010.
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[8] Aguwa JI. Structural Reliability Analysis of the Nigerian Ekki Timber Bridge Beam Subjected to Deflection under the Ultimate Limit State of Loading, presented and published in the Book of Proceedings. 2nd Bienn. Eng. Conf. Titled Energy, Glob. Environ. Chang. Food Secur. Eng. Infrastructure, Organ. by Sch. Eng. Eng. Technol. Fed. Univ. Technol. Minna, Niger., 2011, p. 311–8.
8
[9] Ajamu SO. Optimal design of cement-lime plastered straw bale masonry under vertical load and thermal insulation for a residential building, Ph. D Thesis report submitted to the department of Civil engineering, faculty of Engineering and Technology, University of Il. Ph. D Thesis report submitted to the department of Civil engineering, faculty of Engineering and Technology, University of Ilorin, 2014.
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
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12
[13] Ghasemi SH, Nowak AS. Reliability index for non-normal distributions of limit state functions. Struct Eng Mech 2017;62:365–72.
13
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14
[15] 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:10.5897/JCECT11.052.
15
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[18] Jimoh AA, Rahmon RO, Joseph SG. Evaluation of compressive strength characteristics of structural-sized Apa (Afzelia bipindensis) and Opon (Lannea schimperi) timber species columns found in Nigeria. J Appl Sci Environ Manag 2018;21:1281. doi:10.4314/jasem.v21i7.10.
18
[19] Osuji SO, Nwankwo E. Investigation into the Physical and Mechanical Properties of Structural Wood Commonly Used in Nigeria: A Case Study of Benin City. J Civ Eng Res 2017;7:131–6. doi:10.5923/j.jce.20170705.01.
19
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20
[21] Nabade AM. Development of Strength Classes for Itako (Strombosia pustulata), Oporoporo (Macrocarpa bequaertii), Opepe (Nauclea diderrichii) and Ijebu (Entandrophragma cyclindricum) Nigerian Timber species based on EN 338 (2009). 2012.
21
[22] Jimoh AA, Aina ST. Characterisation and grading of two selected timber species grown in Kwara State Nigeria. Niger J Technol 2017;36:1002–9.
22
[23] Ibitolu BJ, Jimoh AA. Characterization and Grading of some Potential Nigerian Timber Species in accordance to Eurocode EN 338 (2009). 2nd Int. Eng. Conf. (IEC 2017). Fed. Univ. Technol. Minna, Niger., 2017, p. 433–40.
23
[24] Adedeji A. Reliability-Based Probability Analysis for Predicting Failure of Earth Brick Wall in Compression. Niger J Constr Technol Manag 2008;9:25–34.
24
[25] Abdulraheem KK. Reliability Index Assessment of Solid and laminated teak Wooden Deep I-Beam for residential Building. M. Eng. Thesis report submitted to the Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Nigeria. Thesis report submitted to the Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Nigeria, 2016.
25
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26
ORIGINAL_ARTICLE
An Analysis of Sight Distances Considering Both the Vertical and Horizontal Curves of a Tourist Bound Destination Highway in Camarines Sur: The Lagonoy-Presentacion Section
This analyzed sight distances contemplating both vertical and horizontal curves of a tourist bound destination highway in Camarines Sur, particularly the Lagonoy to Presentacion section. The Quantum Geographic Information System (QGIS) was used. The data were validated through site observation. The radius, tangent and sight distances for horizontal curves were obtained through graphical measurement while the elevations, length, slopes of both forward and back tangents, and sight distances of vertical curves were computed using mathematics formula. The decision sight distance and the equivalent maximum speed values were deduced through the policies imposed by the American Association of State Highway and Transportation Officials (AASHTO [1]). The highway has numerous horizontal and vertical curves with radius, tangent distances, intersecting angles, curve lengths; elevations of point of curvature (PC), point of tangencies (PT), and point of intersections (PI); and slope of forward and back tangent causal to short sight distances, delimiting car speeds. Through the obtained sight distance data, the maximum speed limit map was completed.
https://www.jsoftcivil.com/article_57946_cf9922cc374f85945b7c612fd8186684.pdf
2018-04-01
116
126
10.22115/scce.2018.100391.1028
Sight distance
Vertical curve
Horizontal curve
Highway
Maximum speed
Raymundo
Romero
munding25@yahoo.com.ph
1
Professor, College of Engineering and Technology, Partido State University, Goa, Camarines Sur, Philippines
LEAD_AUTHOR
[1] A Policy on geometric design of highways and streets. 2001.
1
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2
[3] Hassan Y, Sayed T. Effect of driver and road characteristics on required preview sight distance. Can J Civ Eng 2002;29:276–88. doi:10.1139/l02-002.
3
[4] Abbas SKS, Adnan MA, Endut IR. An Investigation of the 85th Percentile Operating Speed Models on Horizontal and Vertical Alignments for TwoLane Rural Highways: A Case Study. J - Inst Eng Malaysia 2012;73:31–40.
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[6] S.A. Shebl. Single reverse and unsymmetrical vertical curve for highways utilizing quintic polynomial equation of odd powers. J Comput Appl Math 2015;4:1–7. doi:10.4172/2168-9679.1000257.
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[7] Chen T, Zhang M, Wei L. Driver Behavior on Combination of Vertical and Horizontal Curves of Mountainous Freeways. Math Probl Eng 2014;2014:1–9. doi:10.1155/2014/432841.
7
[8] Bassan S. Sight distance restriction on highways’ horizontal curves: insights and sensitivity analysis. Eur Transp Res Rev 2016;8:21. doi:10.1007/s12544-016-0208-6.
8
[9] Castro M, Iglesias L, Sánchez JA, Ambrosio L. Sight distance analysis of highways using GIS tools. Transp Res Part C Emerg Technol 2011;19:997–1005. doi:10.1016/j.trc.2011.05.012.
9
[10] P. Discett. Analysis on desired speed and traffic sign sight distance. Mod Traffic Transp Eng Res 2014:48–53.
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[11] Al-Kaisy A, Krieder T, Pothering R. Speed selection at sites with restrictive alignment: the US-191 case study. Adv Transp Stud 2013;29:71–82.
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12
[13] Ali MZA, Easa SM, Hamed M. Stop-Controlled Intersection Sight Distance: Minor Road on Tangent of Horizontal Curve. J Transp Eng 2009;135:650–7. doi:10.1061/(ASCE)0733-947X(2009)135:9(650).
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[14] Easa SM. Design considerations for highway reverse curves. Transp Res Rec 1994:1–11.
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[15] Matsoukis E. A parsimonious model for the safety assessment of horizontal curves using data from rural roads. WIT Press; 2011. doi:10.2495/SAFE110041.
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[16] Jalal Kamali MH, Monajjem MS, Ayubirad MS. Studying the effect of spiral curves and intersection angle, on the accident ratios in two-lane rural highways in Iran. PROMET - Traffic&Transportation 2013;25. doi:10.7307/ptt.v25i4.332.
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[18] Zhang Y. Analysis of the Relation between Highway Horizontal Curve and Traffic Safety. 2009 Int. Conf. Meas. Technol. Mechatronics Autom., IEEE; 2009, p. 479–81. doi:10.1109/ICMTMA.2009.511.
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[19] Dabbour E. Optimizing Highway Profiles for Individual Cost Items. Int J Traffic Transp Eng 2013;3:440–7.
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[20] Sun J, Chen C. Length requirements for new single-arc unsymmetrical crest vertical curve for highways. Adv Mater Res 2015;1065:755–9.
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[21] de Oña J, Garach L, Calvo F, García-Muñoz T. Relationship between Predicted Speed Reduction on Horizontal Curves and Safety on Two-Lane Rural Roads in Spain. J Transp Eng 2014;140:04013015. doi:10.1061/(ASCE)TE.1943-5436.0000624.
21
[22] Luque R, Castro M. Highway Geometric Design Consistency: Speed Models and Local or Global Assessment. Int J Civ Eng 2016;14:347–55. doi:10.1007/s40999-016-0025-2.
22
[23] Harwood DW, Bauer KM. Effect of Stopping Sight Distance on Crashes at Crest Vertical Curves on Rural Two-Lane Highways. Transp Res Rec J Transp Res Board 2015;2486:45–53. doi:10.3141/2486-06.
23