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An InteriorConstraint BEM for Regularization of Problems with Improper Boundary Conditions
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A wellposed problem in analysis of elastic bodies requires the definition of appropriate constrains of the boundary to prevent rigid body motion. However, one is sometimes presented with the problem of nonselfequilibrated tractions on an elastic body that will cause rigid body motion, while the boundary should remain unconstrained. One such case is the analysis of multiparticle dynamics where the solution is obtained in a quasistatic 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 InteriorConstraint Boundary Element techniques for elastostatic analysis with improper boundary supports. In the proposed method rigid body modes are eliminated by imposing constrains 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.
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Grady
Mathews IV
Assistant Professor, Department of Civil Engineering, Penn State Harrisburg, Middletown, PA 17057, USA
Assistant Professor, Department of Civil
United States
gfm5121@psu.edu


Dimitris
Rizos
Associate Professor, Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
Associate Professor, Department of Civil
United States
rizos@engr.sc.edu


Robert
Mullen
Professor, Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
Professor, Department of Civil and Environmental
United States
rlm@sc.edu
Boundary element method
Rigid body motion constrains
Regularization
Approximate solutions
[[1] Cundall PA and Strack ODL. A discrete numerical model for granular assemplies. Geotechnique 29(1), 1979, 4765. ##[2] Pöschel T and Thomas S. Computational Granular Dynamics: Models and Algorithums.: Springer; 2005. ##[3] Mathews GF. Developments for the Advancement of the Discrete Element Method. PhD Dissertation. Department of Civil and Environmental Engineering, University of South Carolina, 2015. ##[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. ##[5] Blázquez A, Mantič V, París F, Cañas J. On the removal of rigid body motions in the solution of elastostatic problems by the direct BEM. Int J Numer Methods Eng 1996;39:4021–38. doi:10.1002/(SICI)10970207(19961215)39:233.0.CO;2Q. ##[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. ##[7] Vodička R, Mantič V, París F. On the removal of the nonuniqueness in the solution of elastostatic problems by symmetric Galerkin BEM. Int J Numer Methods Eng 2006;66:1884–912. doi:10.1002/nme.1605. ##[8] Fredholm I. Sur une classe d’équations fonctionnelles (On a class of functional equations). Acta Math 1903;27:365–90. doi:10.1007/BF02421317. ##[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. ##[10] Rump SM. Inversion of extremely Illconditioned matrices in floatingpoint. Jpn J Ind Appl Math 2009;26:249–77. doi:10.1007/BF03186534. ##[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/S00207683(97)002618. ##[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. ##[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/S00457949(03)000130. ##[14] Xiao YX, Zhang P, Shu S. An algebraic multigrid method with interpolation reproducing rigid body modes for semidefinite problems in twodimensional linear elasticity. J Comput Appl Math 2007;200:637–52. doi:10.1016/j.cam.2006.01.021. ##[15] Ko YY, Chen CH. Application of Symmetric Galerkin Boundary Element Method on Elastostatic Neumann Problems. Int. Assoc. Comput. Methods Adv. Geomech., Goa, India: 2008, p. 146–53. ##[16] Rizzo FJ. An integral equation approach to boundary value problems of classical elastostatics. Q Appl Math 1967;25:83–95. ##[17] Wagdy M, Rashed YF. Boundary element analysis of multithickness sheardeformable slabs without subregions. Eng Anal Bound Elem 2014;43:86–94. doi:10.1016/j.enganabound.2014.03.011. ##[18] Lamé G. Leçons sur la théorie mathématique de l’élasticité des corps solides (Lessons on the mathematical theory of elastic solids). Paris: GauthierVillars; 1852. ##[19] Brebbia CA, Dominguez J. Boundary Elements: An Introductory Course. WIT Press; 1996. ##[20] Cauchy A. Sur un nouveau genre de calcul analogue au calcul infinitesimal (On a new type of calculus analogous to the infinitesimal calculus). Oeuvres Complet d’Augustin Cauchy, GauthierVillars, Paris 1826. ##[21] Thompson (Lord Kelvin) W. Note on the integration of the equations of equilibrium of an elastic solid. Cambridge Dublin Math J 1848:87–9. ##[22] Kronecker L. Vorlesungen über die Theorie der Determinanten: Erste bis Einundzwanzigste Vorlesungen (Lectures on the theory of determinants : First to Twentyfirst lectures). B. G. Teubner; 1903. ##[23] Banerjee P. The Boundary Element Methods in Engineering. London: McGrawHill; 1994. ##[24] Mineur H, BerthodZaborowski H, Bouzitat J, Mayot M. Techniques de calcul numérique à l’usage des mathématiciens, astronomes, physiciens et ingénieurs (Numerical Computation techniques to use of Mathematicians, Astronomers, Physicists and Engineers). Liège Libr Polytech Béranger, Paris 1952. ##[25] Hadamard J. Le problème de Cauchy et les équations aux dérivées partielles linéaires hyperboliques (The Cauchy problem for linear equations and hyperbolic partial differential). Paris: Hermann & Cie.; 1932. ##[26] Dassault. Abaqus 6.8 Program 2008. ##[27] Slaughter W. The linearized theory of elasticity. Birkhäuser; 2002. ##]
Comparative Analysis of Rigid Pavement using Westergaard Method and Computer Program
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Country’s economic, social and cultural development is mainly dependent on 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 variation of traffic loading, material properties and climatic conditions. The main objective of this project is to make 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 significant impact on developed stresses at different slab locations.
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Intisar
AlGhafri
Department of Civil and Environmental Engineering, University of Nizwa,Oman
Department of Civil and Environmental Engineering,
Oman
07008082@uofn.edu.om


Muhammad
Javid
Department of Civil and Environmental Engineering,University of Nizwa, Oman
Department of Civil and Environmental Engineering,
Oman
ma.javid@hotmail.com
Rigid pavement
Westergaard Method
Traffic Loading
Curling Stresses
Profiled Composite Slab Strength Determination Method
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2
Abstract: 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 reliabilitybased evaluation of PCS load capacity design with longitudinal shear estimation under slopeintercept (mk) method. The limitstate 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 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 experimentbased test results indicates high similarity, demonstrating the viability of the proposed strength determination methodology.
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Kachalla
Mohammed
Senior Lecturer, Department of Civil & Water Resources Engineering, University of Maiduguri, Maiduguri, Nigeria
Senior Lecturer, Department of Civil &
Nigeria
engrkachalla@unimaid.edu.ng


Izian
Karim
Senior lecturer, Department of Civil Engineering, University Putra Malaysia, Serdang, Malaysia
Senior lecturer, Department of Civil Engineering,
Malaysia
izian_abd@upm.edu.my


Farah
Aziz
Associate Professor, Department of Civil Engineering, University Putra Malaysia, Malaysia
Associate Professor, Department of Civil
Malaysia
farah@upm.edu.my


Teik Hua
Law
Associate Professor, Department of Civil Engineering, University Putra Malaysia, Malaysia
Associate Professor, Department of Civil
Malaysia
lawteik@upm.edu.my
Slopeintercept method
Reliability
Profiled composite slab
Longitudinal shear
First order reliability method
Strength test
Stream Flow Forecasting using Least Square Support Vector Regression
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Effective stream flow forecast for different leadtimes is useful in almost all water resources related issues. The Support Vector Machines (SVMs) are learning systems that 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 with respect to the kernel used in SVM on data points such that the points lie between two marginal hyper planes which helps in minimizing the regression error. The current paper presents least square support vector regression (LSSVR) to predict one day ahead stream flow using past values of the rainfall and stream flow at three stations in India, namely Nighoje and Budhwad in Krishna river basin and Mandaleshwar in Narmada river basin. The relevant inputs are fixed on the basis of autocorrelation, Crosscorrelation and trial and error. The model results are reasonable as can be seen from low value of Root Mean Square Error (RMSE), Coefficient of Efficiency (CE) and Mean Absolute Relative Error (MARE) accompanied by scatter plots and hydrographs.
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88


Shreenivas
Londhe
Professor, Vishwakarma Institute of Information Technology, Pune, India
Professor, Vishwakarma Institute of Information
India
shreenivas.londhe@viit.ac.in


Seema
Gavraskar
PG Student, Vishwakarma Institute of Information Technology, Pune, India
PG Student, Vishwakarma Institute of Information
India
seemagavraskar@gmail.com
Stream flow forecasting
Support Vector Machines
Support vector regression
Kernel function
[[1] Shalamua A. Monthly and Seasonal Streamflow Forecasting in the Rio Grande Basin, Ph.D. thesis, New Mixico State University, 2009, 321. ##[2] Bhatnagar A. Hydrologic Time Series Analysis using Support Vector Regression, M.Tech Thesis2009, Indian Institute of Technology, Bombay ##[3] Solomatine D. P., Wagener T. Hydrological Modeling, Treatise on Water Science, 2011; 2: 435457. ##[4] Shreshta R.R., Nestmann M. Physically Based and DataDriven Models and Propagation of Input Uncertainties in River Flood Prediction.ASCE Hydrologic Engineering, 2009; 4(12): 14091419. ##[5] http://www.cwc.gov.in/main/HP ##[6] www.mahap.org ##[7] Mahjoobi E. J., Adeli M. Prediction of significant wave height using regressive support vector machines. Ocean Engineering, 2009; 36(5): 339347. ##[8] Vapnik V.N. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999; 10 (5): 988–999. ##[9] Dibike Y. B., Velickov S., Solomatine, Michael B., A. Model Induction with Support Vector Machines: Introduction and Applications. ASCE Journal of Computing in Civil Engineering, 2001; 15(3): 208216. ##[10] Wu C. L., Chau K. W., Li Y. S. River stage prediction based on a distributed support vector regression. Journal of Hydrology, 2008; 358: 96– 111. ##[11] Suykens J. A. K., Vandewalle Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 1999; 9:293300. ##[12] Rajsekaran S., Gayathri S. T., Lee T. L. Support Vector regression methodology for storm surge prediction. Ocean Engineering, 2008;35(16) :15781587. ##[13] Dibike Y. B., Velickov S., Solomatine D. Support Vector Machines: Review and Applications in Civil Engineering, Proc. of the joint workshop on Applications of AI in Civil Engineering, Cottbus2000, Germany. ##[14] Bray M., Han D. Identification of support vector machines for runoff modeling. Journal of Hydroinformatics, 2004;6(4): 265280. ##[15] Asefa T., Kemblowski M., Mckee M, Khalil A. Multitime scale stream flow predictions: The support vector machines approach. Journal of Hydrology, 2006; 318(14): 716. ##[16] Lin JianYi, Cheng ChunTia, Chau, KwokWing. Using support vector machines for longterm discharge prediction. Hydrological Sciences Journal, 2010; 51(4): 599612. ##[17] Yu P., Chen S, Chang I. Support vector regression for realtime flood stage forecasting. Journal of Hydrology, 2006; 328: 704716. ##[18] Behzad M., Asghari K., Eazi M., Palhang M. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with applications, 2009; 36(4): 76247629. ##[19] Noori R., Karbassi A. R., Moghaddamnia A., Han D., ZokaeiAshtiani M. H., Farokhnia A., Gousheh, M. G. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology. 2011;401(3): 177189. ##[20] Kisi O. Modelling DischargeSuspended Sediment Relationship using Least Square Support Vector Machine. Journal of Hydrology, 2012; 456–457:110–120. ##[21] Bhagwat P., Maity R. Hydroclimaticstreamflow prediction using Least SquareSupport Vector Regression. ISH Journal of Hydraulic Engineering, 2013; 19(3): 320–328. ##[22] Sahraei S., Andalani S. Z., Zakermoshfegh M., Sisakht B. N., Talebbeydokhti N., Moradkhani, H. Daily discharge forecasting using least square support vector regression and regression tree. ScientiaIranica. Transaction A, Civil Engineering, 2015; 22(2): 410422. ##[23] Kalteh A. M. Wavelet Genetic AlgorithmSupport Vector Regression (wavelet GASVR) for Monthly Flow Forecasting. Water Resources Management, 2015; 29 (4): 1283–1293. ##[24] Kalteh A. M. Monthly river flow forecasting using Artificial Neural Network and Support Vector Regression models Coupled with Wavelet Transform. Computers and Geosciences, 2013; 54: 1–8. ##[25] Kalteh A. M., Improving Forecasting Accuracy of StreamflowTime Series using Least Squares Support Vector Machine Coupled with DataPreprocessing techniques. Water Resources Management, 2016; 30 (2): 747–766. ##[26] Kisi O.Stream Flow Forecasting and Estimation using Least Square Support Vector Regression and Adaptive NeuroFuzzy Embedded Fuzzy Cmeans Clustering. Water Resources Management, 2015; 29(14): 5109–5127. ##[27] Zamini A., Solomatine D, Azimian A, Heemink A. Learning from data for wind wave forecasting. Ocean engineering, 2008; 35: 953962. ##[28] Londhe S., N., Dixit P.,R., Charhate S. B. Forecasting Ocean Waves using Support Vector Regression. Proc. of 18thIAHRAPD2012 2012, Jeju, South Korea, 3841. ##[29] Legates D., McCabe G., J. Evaluating the use of GoodnessofFit Measures in hydrologic and hydroclimatic model validation. Water Resources Research, 2010;35(1): 233241. ##[30] Dawson C.W., Wilby R. L. Hydrological modeling using artificial neural networks. Progress in Physical Geography, 2001; 25(1): 80108. ##[31] Londhe S., N., Panchang V. OneDay Wave Forecasts Based on Artificial Neural Networks. Journal of Atmospheric and Oceanic Technology, 2006; 23: 1593–1603.##]
Reliability Analysis of Structures Using Modified FA_PSO Algorithm
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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 noconfidence; 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.
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Hamidreza
Shahmoradi Qomi
Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Student, Faculty of Civil Engineering,
Iran
phd.shahmoardi@gmail.com


Pavlo
Voitenko
Ph.D. Candidate, Graduate Research Assistant, Department of Civil Engineering, Auburn University, Auburn, United States
Ph.D. Candidate, Graduate Research Assistant,
United States
pzv0011@auburn.edu


Majid
Taheri
Ph.D. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran
Ph.D. Student, Faculty of Civil Engineering,
Iran
majidtaheri@semnan.ac.ir
Reliability Index
Engineering Problems
Limit State Function
Modified FA_PSO Algorithm
[[1] Santosh, T., R. Saraf, A. Ghosh, and H. Kushwaha, (2006) Optimum step length selection rule in modified HL–RF method for structural reliability. International Journal of Pressure Vessels and Piping. 83(10): p. 742748. ##[2] Melchers, R., Structural Reliability: Analisis and Prediction.(1987. 1987: Ellis Horwood, John Wiley. ##[3] Durbin, R., J. Sulston, and S. Fraser, (1987) AREAS OF EXPERTISE. Biochemistry. 6: p. 1990. ##[4] Forsell, C., (1924) Economy and construction. Sunt Förnuft. 4: p. 7477. ##[5] Freudenthal, A.M., (1947) The safety of structures. Transactions of the American Society of Civil Engineers. 112(1): p. 125159. ##[6] Johnson, A.I., Strength, safety and economical dimensions of structures.(1953. 1953: Petterson. ##[7] Hasofer, A.M. and N.C. Lind, (1974) Exact and invariant secondmoment code format. Journal of the Engineering Mechanics division. 100(1): p. 111121. ##[8] Liu, P.L. and A. Der Kiureghian, (1991) Optimization algorithms for structural reliability. Structural safety. 9(3): p. 161177. ##[9] Melchers, R.E., (2003) Probabilistic model for marine corrosion of steel for structural reliability assessment. Journal of Structural Engineering. 129(11): p. 14841493. ##[10] Cheng, J., (2007) Hybrid genetic algorithms for structural reliability analysis. Computers & Structures. 85(19): p. 15241533. ##[11] Kirkegaard, P.H., J.D. Sørensen, D. Čizmar, and V. Rajčić, (2011) System reliability of timber structures with ductile behaviour. Engineering structures. 33(11): p. 30933098. ##[12] Jahani, E., M.A. Shayanfar, and M.A. Barkhordari, (2013) A new adaptive importance sampling Monte Carlo method for structural reliability. KSCE Journal of Civil Engineering. 17(1): p. 210. ##[13] Eamon, C.D. and E. Jensen, (2013) Reliability analysis of RC beams exposed to fire. Journal of Structural Engineering. 139(2): p. 212220. ##[14] Xiao, N.C., Y.F. Li, Y. Yang, L. Yu, and H.Z. Huang, (2014) A novel reliability method for structural systems with truncated random variables. Structural Safety. 50: p. 5765. ##[15] Chojaczyk, A., A. Teixeira, L.C. Neves, J. Cardoso, and C.G. Soares, (2015) Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety. 52: p. 7889. ##[16] Ghasemi, S.H. and A.S. Nowak3a, (2017) Reliability index for nonnormal distributions of limit state functions. Structural Engineering and Mechanics. 62(3): p. 000000. ##[17] Ghasemi, S.H. and A.S. Nowak, (2016) Mean maximum values of nonnormal distributions for different time periods. International Journal of Reliability and Safety. 10(2): p. 99109. ##[18] Ghasemi, S.H. and A.S. Nowak, (2017) Target reliability for bridges with consideration of ultimate limit state. Engineering Structures. 152: p. 226237. ##[19] Yanaka, M., S. Hooman Ghasemi, and A.S. Nowak, (2016) Reliability‐based and life‐cycle cost‐oriented design recommendations for prestressed concrete bridge girders. Structural Concrete. 17(5): p. 836847. ##[20] Elegbede, C., (2005) Structural reliability assessment based on particles swarm optimization. Structural Safety. 27(2): p. 171186. ##[21] Kaveh, A., M. Massoudi, and M.G. Bagha, (2014) STRUCTURAL RELIABILITY ANALYSIS USING CHARGED SYSTEM SEARCH ALGORITHM*. Iranian Journal of Science and Technology. Transactions of Civil Engineering. 38(C2): p. 439. ##[22] B. Keshtegar , M.M., (2014) A NEW METHOD FOR ASSESMENT OF THE STRACTURAL RELIABILITY. Journal of Modeling in Engineering. p. 2942. ##]
Modeling of Compressive Strength Characteristics of Structuralsized Afara (Terminalia superba) and Babo (Isoberlinia doka) Timber Columns Using Constant Failure Rate (CFR) Model of Reliability
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This paper investigated the reliability of the Structuralsized 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 509.80 and 849.67 kg/m3 respectively. 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 design of Afara column and Babo column are σ=〖16.992e〗^(0.0039λ) 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 respectively 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 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.
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Alao
Jimoh
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, Faculty
Nigeria
aajimoh4real@yahoo.com


Rauf
Rahmon
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, Faculty
Nigeria
rorahmon2222@gmail.com


Khalid
Ibrahim
Department of Civil Engineering, Faculty of Engineering and Technology, University of Ilorin, Ilorin, Nigeria
Department of Civil Engineering, Faculty
Nigeria
awadrada@gmail.com
Afara
Babo
Compressive strength
regression analysis
Reliability
An Analysis of Sight Distances Considering Both the Vertical and Horizontal Curves of a Tourist Bound Destination Highway in Camarines Sur: The LagonoyPresentacion Section
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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 accorded to short sight distances which delimit car speeds to avoid accident. Through the obtained sight distance data, the maximum speed limit map was completed.
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126


Raymundo
Romero
College of Engineering and Technology, Partido State University, Goa, Camarines Sur Philippines
College of Engineering and Technology, Partido
Philippines
munding25@yahoo.com.ph
Sight distance
Vertical curve
Horizontal curve
Highway
Maximum speed