2015
2
3
3
103
1

Estimating the Cost of Road Traffic Accidents in Iran using Human Capital Method
http://www.ijte.ir/article_9601.html
10.22119/ijte.2015.9601
1
Road traffic accidents and the effects they subsequently incur are increasing dramatically, and estimating the cost of road traffic crashes could be a vital step in improving the recognition of this widespread problem. The main objective of this paper is to estimate the cost of road traffic crashes in Iran using the Human Capital (HC) Method. Results of this study indicate that the cost of road traffic crashes in Iran for the year 2009 was approximately 114,455 billion Iranian Rials (about $US 11.458 billion), which accounted for 1.41% of Iran’s Gross National Product (GNP) in that year. This study shows that lost output and property damage account for the largest proportion of this cost, whereas human, administration and medical costs are the next highest contributors. In addition, this study estimates the cost of accidents in Iran for the year 2009, depending on their status as fatal, causing serious injury, slight injury and propertydamageonly.
0

163
178


Mohammad Reza
Ahadi
Road, Housing & Urban Development Research Center
Iran
ahadireza@yahoo.com


Hesamoddin
RaziArdakani
Department of Civil Engineering, Sharif University of Technology
Iran
hesam.razi@gmail.com
Human Capital Method
crashes
costs
Developing countries
1

ProbitBased Traffic Assignment: A Comparative Study between LinkBased Simulation Algorithm and PathBased Assignment and Generalization to RandomCoefficient Approach
http://www.ijte.ir/article_9602.html
10.22119/ijte.2015.9602
1
Probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. The problem of path overlapping in network modelling has been one of the main issues to be tackled. Due to its flexible covariance structure, probit model can adequately address the problem. Despite that probit is one of the most appealing choice models, due to the lack of closed form expressions for evaluating choice probabilities; it has not received extensive attention by network modeling researchers. This study is set out to focus on this approach of traffic assignment. Computational difficulty of application of probit model in the largescale network equilibrium problem has triggered development of some linkbased probit network loading methods which exempt the analyst from generating and maintaining pathflow variables explicitly. To the best of our knowledge, however, the bias of these heuristic linkbased methods has not been studied so far. This contribution primarily focuses on investigation of such potential bias in linkbased probit assignment methods. In this research, this bias for a certain simulated linkbased method proposed in the literature is empirically considered and investigated through comparison with pathbased probit equilibrium solution. Our findings indicate considerable level of bias for the examined linkbased algorithm. Capable of representing utility correlation and heteroscedasticity, probit model has always been one of the most theoretically attractive models for representing route choice behavior. However, this soundness of theory could further be enhanced through combining the ideas of probit and randomcoefficient modeling which enables the analyst to capture random taste heterogeneity over travelers as well. To do this, the notion of mixed probit model, as a generalization to classical fixedcoefficient probit, is introduced and applied to an illustrative network in this study, in addition to the main contribution of the article.
0

179
198


Milad
Haghani
Department of Civil Engineering, Institute of Transport Studies, Monash University
Iran
milad.haghani@monash.edu


Zahra
Shahhoseini
Department of Civil Engineering, Institute of Transport Studies, Monash University
Iran
zahra.shahhoseini@ymail.com


Majid
Sarvi
Department of Civil Engineering, Institute of Transport Studies, Monash University
Iran
majid.sarvi@monash.edu
Probit Model
Multivariate Normal Distribution
Monte Carlo simulation
randomcoefficient choice models
LinkBased and PathBased Traffic Assignment
[ Akamatsu, T. (1996) “Cyclic flows, Markov process and stochastic traffic assignment”, Transportation Research 30B(5), pp.369386.## Bekhor S., T. Toledo, and L. Reznikova. (2008) “A PathBased Algorithm for the CrossNested Logit Stochastic User Equilibrium Traffic Assignment”, ComputerAided Civil and Infrastructure Engineering, Vol 24, pp. 1525.## ## Bekhor, S., M. E. BenAkiva, and M. S. Ramming. (2006) “Evaluation of Choice Set Generation Algorithms for Route Choice Models”, Ann Oper Res, 144, pp. 235247.## ## Bekhor, S., M. S. BenAkiva, and S. M. Ramming. (2002) “Adaptation of Logit Kernel to Route Choice Situation”, In Transportation Research Record: Journal of the Transportation Research Board, No. 1805, TRB, National Research Council, Washington, D.C., pp. 78–85.## Bell, M. G. H. (1995) “Alternatives to Dial's Logit Assignment Algorithm”, Transportation Research 29B(4), pp.287296.## BenAkiva, M. and Bierlaire, M. (1999) “Discrete choice methods and their applications to short term travel decisions”, in: W. Hall (Ed.) “Handbook of Transportation Science”, pp. 5–33 (Dordrecht: Kluwer).## ## Bovy, P. H. L., S. Bekhor, and G. Prato. (2008) “The factor of revisited path size: Alternative derivation. In Transportation Research Record”, Journal of the Transportation Research Board, No. 2076, Transportation Research Board of the National Academies, Washington, D.C., pp. 132–140.## Burrell, J. E. (1968) “Multiple road assignment and its application to capacity restraint”, Proc., 4th. International Symposium on the Theory of Traffic Flow, Karlsruhe, West Germany, pp. 210–219.## Cascetta, E., Nuzzolo, A., Russo, F. and Vitetta, A. (1996) “A modified logit route choice model overcoming path overlapping problems: Specification and some calibration results for Interurban Networks”, In Proc., 13th International Symposium on Transportation and Traffic Theory, Pergamon, Lyon, France, pp. 697–711.## Clark, C. E. (1961) “The greatest of a finite set of random variables”, Operation Research, Vol.9, pp. 145162.## Clark, S. D. and Watling, D. P. (2000) “Probitbased sensitivity analysis for general traffic networks”, Transportation Research Record, 1733, pp. 8895.## Daganzo, C. F. and Sheffi, Y. (1977) “On stochastic models of traffic assignment”, Transportation Science, Vol. 11, pp. 253–274.## Dial, R. B. (1971) “A probabilistic multipath traffic assignment algorithm which obviates path enumeration”, Transportation Research, Vol. 5, pp. 83–111.## Eaton, M. L. (1983) “Multivariate statistics: a vector space approach”, John Wiley and Sons..## Fisk, C. (1980) “Some developments in equilibrium traffic assignment”, Transportation Research, Vol. 14B, pp. 243–255.## ## Florian, M. (1974) “On modeling congestion in dialʼs probabilistic assignment model”, Transportation Research, Vol. 8, pp. 8586.## Florian, M., and B. Fox. (1976) “On the Probabilistic Origin of Dialʼs Multipath Traffic Assignment Model”, Transportation Research, Vol. 10, pp. 339341.## Haghani, M., Shahhoseini, Z., Samimi, A. and Aashtiani, A. Z. (2013) “On calibration and application of logitbased stochastic traffic assignment models”, International Journal of Transportation Engineering (IJTE), Vol.13, pp. 151172.## Horn, R. A. and Johnson, C. R. (1985) “Matrix analysis”, Cambridge University Press.## Horowitz, J. L., Sparmann, J. M and. Daganzo, C. F (1982) “An investigation of the accuracy of the Clark approximation for the multinomial probit model”, Transportation Science, Vol. 16 (3), pp. 382401.## Haghani, M., Shahhoseini, Z. and Sarvi, M. (2014) “Pathbased stochastic traffic assignment: an investigation on the effect of choicesets size, model specification and model calibration on prediction of static flow,93rd Annual Meeting of Transportation Research Board (TRB), Washington, DC.## Maher, M. (1992) “SAM—A stochastic assignment model”, In Mathematics in Transport Planning and Control (J. D. Griffiths, ed.), Oxford University Press, pp. 121–132.## Maher, M. and Hughes, P. C (1997) “A Probit based stochastic user equilibrium assignment model”, Transportation Research, Vol. 31B, pp. 341–355.## Nguyen, S. and Dupuis, D. (1984) “An efficient method for computing traffic equilibria in networks with asymmetric transportation costs”, Transportation Science, Vol.18, pp. 185202.## Prashker, J. N. and Bekhor, S. (1998) “Investigation of stochastic network loading procedures”, Transportation Research Record, 1645, pp. 94–102.## Prashker, J. N. and Bekhor, S. (2000) “Congestion, stochastic, and similarity effects in stochastic userequilibrium models”. In Transportation Research Record: Journal of the Transportation Research Board, No. 1733, TRB, National Research Council, Washington, D.C., pp. 80–87.## Prato, C. G., and Bekhor, S. (2006) “Applying branchandbound technique to route choice set generation. in transportation research record”, Journal of Transportation Research Board, No. 1985, Transportation Research Board of the National Academies, Washington, D.C., pp. 19–28.## Prato, C. G., and Bekhor, S. (2007) “Modeling route choice behavior: how relevant is the composition of choice set?”. In Transportation Research Record, Vol. 2003, pp. 6473.## Rice, J. (1995) “Mathematical statistics and data analysis(second ed.)”, Duxbury Press.## Rosa, A. (2003) “Probit based methods in traffic assignment and discrete choice modeling”, Ph.D. Dissertation, Napier University.## Sheffi, Y. and Powell, W.B. (1981) “A comparison of stochastic and deterministic traffic assignment over congested networks”, Transportation Research, Vol.15B, pp. 5364.## Sheffi, Y. and Powell, W. B. (1982) “An algorithm for the equilibrium assignment problem for random link times”, Networks, 12(2), pp. 191207.## Train, K. E. (2009) “Discrete choice methods with simulation”, Cambridge University Press, New York.## Von Falkenhausen, H. (1966) “Traffic assignment by a stochastic model”, Proceedings, 4th International Conference on Operational Science, pp. 415421.## Yai, T., Iwakura, S.and Morichi, S. (1997) “Multinomial probit with structured covariance for route choice behavior”, Transportation Research, Vol. 31B, pp. 195–207.## Zhang, K., Mahmassani, H. S. and Lu, C. C. (2008) “Probitbased timedependent Stochastic user equilibrium traffic assignment model”, Transportation Research Record, 2085, pp. 8694.##]
1

Rock Slope Stability Analysis Using Discrete Element Method
http://www.ijte.ir/article_9603.html
10.22119/ijte.2015.9603
1
Rock slope stability depends very much on the strength features of the rock and the geometrical and strength characteristics of the discontinuities (e.g., roughness, wall strength and persistence). Since a rock mass is not a continuum, its behavior is dominated by such discontinuities as faults, joints and bedding planes. Also, Rock slope instability is a major hazard for human activities and often causes economic losses, property damage (maintenance costs), as well as injuries or fatalities. A computer program has been developed in this research study to perform the stability analysis of a rock slope using the Discrete Element Method (DEM). The rock in the present model is treated as some blocks connected together by elastoplastic Winkler springs. This method, the formulation of which satisfies all equilibrium and compatibility conditions, considers the progressive failure and is able to find the slip surface or unstable blocks. To demonstrate the applicability and usefulness of the method, several examples have been presented for the analysis and optimization of the rock slope stabilization.
0

199
212


Mohammad Reza
Maleki Javan
University of Tehran
Iran
malkijavan@ut.ac.ir


Fouad
Kilanehei
Department of Civil Engineering, Imam Khomeini International University
Iran


Amir
Mahjoob
Road, Housing & Urban Development Research Center
Iran
amahjoob@ut.ac.ir
rock slope stability
discrete element method
Limit equilibrium
[ Chang, C. S. (1991) "Discrete element method for bearing capacity analysis", Computers and Geotechnics, 12, pp. 273288.## Chang, C. S. (1992) "Discrete element method for slope stability analysis", Journal of Geotechnical Engineering, 118(12), pp. 18891905.## Chang, C. S. (1994) "Discrete element analysis for active and passive pressure distribution on retaining walls", Computers and Geotechnics, 16, pp. 291310.## Cundall, P.A. (1987) "Distinct element models of rock and soil structure", Analytical and Computational Methods in Engineering Rock Mechanics. George Allen and Unwin, London, pp. 129– 163.## Easki, T., Jiang, Y., Bhattarai, T.N., Maeda, T., Nozaki, A. and Mizokami, T. (1999) "Modeling jointed rock masses and prediction of slope stabilities by DEM", Proceedings of the 37th U.S. Rock Mech. Symp., Vail, Colorado, June 1999, pp. 83– 90.## Heuze, F.E., Walton, O.R., Maddix, D.M., Shaffer, R.J. and Butkovich, T.R. (1990) "Analysis of explosion in hard rocks: the power of the discrete element modeling”, Mechanics of Jointed and Faulted Rock., Proc. Int. Conf. Vienna Balkema, Rotterdam, pp. 21–28.## Kainthola, A., Singh, P. K., Wasnik, A. B. and Singh, T. N. (2012) "Distinct element modelling of Mahabaleshwar Road cut hill slope", Geomaterials, 2, pp. 105 113## Kim, J. S., Lee, S. R. and Kim, J. Y (1997) "Analysis of soil nailed earth slope by discrete element method", Computers and Geotechnics, 19(1), pp. 114.## Kveldsvik, V., Kaynia, A. M., Nadim, F., Bhasin,R., Bjrn N., Einstein, H. H., (2009), "Dynamic distinct element analysis of the 800 m high Aknes rock slope", International Journal of Rock Mechanics and Mining Sciences, 46(4), pp. 686698.## Lin, Y., Zhu, D., Deng, Q., and He, Q. (2012) "Collapse analysis of jointed rock slope based on UDEC software and practical seismic load", International Conference on Advances in Computational Modelling and Simulation, 31, pp. 441416.## Pal, S., Kaynia, A., Bhasin, A. M. and Paul, R. K. (2012) "Earthquake stability analysis of rock slopes: A case study", Rock Mechanics and Rock Engineering, 45(2), pp. 205–215.## Rathod, G. W., Shrivastava, A. K. and Rao, K. S. (2011) "Distinct element modelling for high rock slopes in static and dynamic conditions: A case study", GeoRisk, ASCE, pp. 484492.## Shen, H., Abbas, S.M. (2013) "Rock slope reliability analysis based on distinct element method and random set theory", International Journal of Rock Mechanics and Mining Sciences, 61, pp. 1522.## Zhang, C., Pekau, O.K., Feng, J. and Guanglun, W. (1997) "Application of distinct element method in dynamic analysis of high rock slopes and blocky structures", Soil Dynamic Earthquake Engineering, 16, pp. 385–394.##]
1

A Monte Carlo Simulation of Chain Reaction Rear End Potential Collisions on Freeways
http://www.ijte.ir/article_9604.html
10.22119/ijte.2015.9604
1
In recent research on modelling road collisions very little attention has been paid to rearend chain reaction collisions, which is characterized by more than two vehicles involved in a collision at the same time. The core aim of the present research is to develop a methodology to estimate such potential collision probabilities based on a proactive perspective, where deceleration rate to avoid collision is used as a surrogate safety measure. In a rearend chain reaction collision the following driver’s response time and the vehicle’s maximum available deceleration rate are both assumed as stochastic causes of collision. To consider the uncertainty of variables in estimating the Nvehicle rearend collision, a methodology based on Monte Carlo simulation is proposed. To show the applicability of the proposed methodology, the NGSIM trajectory database of I80 interstate freeway is used. The probability density function for drivers’ response time is developed through the analysis of 1534 car following situations detected in 45 minutes of movement. The potential risk of two to five vehicle reaction collisions in a five vehicle platoon is estimated by running the simulation through 20 thousand substitutions of randomized generation values drawn from probability density function of response time and maximum available deceleration rate in a following outcome function. Results show that avoiding rearend collision should be considered as a shared responsibility among the drivers in the platoon. As expected, the methodology considers probability of N vehicles colliding at the same time decreasing as N increases. Nvehicle collision is shown to be directly related to the clearance between the following vehicles within the platoon and the speed of individual vehicles as well as the drivers’ reaction time and the maximum deceleration rate available in individual vehicles. The proposed methodology considers all vehicles in the platoon to estimate the risk of potential chain reaction collisions, rather than just simply focusing only on the two leading and following vehicles. Therefore, the proposed methodology is believed to act more effectively than the ordinary methods, particularly if it is used to alarm drivers of vehicles synchronized based on vehicular ad hoc network (VANET) methodologies.
0

213
230


Amir Reza
Mamdoohi
Department of Civil and Environmental Engineering, Tarbiat Modares University
Iran
armamdoohi@modares.ac.ir,


Mohsen
Fallah Zavareh
Department of Civil and Environmental Engineering, Tarbiat Modares University
Iran


Abolfazl
Hassani
Department of Civil and Environmental Engineering, Tarbiat Modares University
Iran


Trond
Nordfjærn
Research Scientist, Norwegian Institute for Alcohol and Drug Research, Oslo, Norway
Iran
Chain reaction rear end collision
Response time
maximum available deceleration rate
Monte Carlo simulation
[American Association of State Highway and Transportation Officials [AASHTO] (2004) "AASHTO Green Book, Policy on Geometric Design of Highways and Streets 2004", 5th ed., Washington: American Association of State Highway & Transportation.## Amundsen, F.H, and C. Hydén (1977) “Proceedings of The First Workshop on Traffic Conflicts”, In Oslo, TTI, Oslo, Norway and LTH Lund, Sweden.## Brackstone, Mark and Mike McDonald, Mike (1999) “Carfollowing: A historical review”, Transportation Research Part F: Traffic Psychology and Behaviour, 2 (4): pp.181–96.##Cunto, Flavio, Craveiro, Jose and Saccomanno, Frank F. (2007) “Microlevel traffic simulation method for assessing crash potential at intersections”, In . http://trid.trb.org/view.aspx?id=802073.## Cunto, Flávio and Saccomanno, Frank F. (2008) “Calibration and validation of simulated vehicle safety performance at signalized intersections”, Accident Analysis & Prevention, 40 (3): pp.1171–79.## Davis, Gary A., Hourdos, John, Xiong, Hui and Chatterjee, Indrajit (2011) “Outline for a causal model of traffic conflicts and crashes”, Accident Analysis & Prevention 43 (6): pp.1907–19.## Davis, Gary A. and Swenson, Tait (2006) “Collective responsibility for freeway rearending accidents?: An application of probabilistic causal models”, Accident Analysis & Prevention 38 (4): pp.728–36.## Del Castillo, J. M., Pintado, P. and Benitez, F. G. (1994) “The reaction time of drivers and the stability of traffic flow”, Transportation Research Part B: Methodological 28 (1): pp.35–60.## Federal Highway Administration [FHWA] (2006) “Interstate 80 Freeway dataset fact sheet”, FHWA HRT06137. United States. http://www.fhwa.dot.gov/publications/research/operations/06137/.## Guido, Giuseppe, Saccomanno, Frank; Vitale, Alessandro; Astarita, Vittorio and Festa, Demetrio (2010) “Comparing safety performance measures obtained from video capture data”, Journal of Transportation Engineering 1 (1): pp.165–165.## Hauer, Ezra and Garder, Per (1986) “Research into the validity of the traffic conflicts technique”, Accident Analysis & Prevention 18 (6): pp. 471–81.##James, F. (1980) “Monte Carlo theory and practice”, Reports on Progress in Physics, 43 (9): pp.1145.##Laureshyn, Aliaksei (2010) “Application of automated video analysis to road user behaviour”, Ph.D. dissertation, Sweden: Lund University.##Laureshyn, Aliaksei, Åse Svensson, and Christer Hydén (2010) “Evaluation of traffic safety, based on microlevel behavioural data: Theoretical framework and first implementation”, Accident Analysis & Prevention, 42 (6): pp.1637–46.##Lord, Dominique and Fred Mannering (2010) “The statistical analysis of crashfrequency data: A review and assessment of methodological alternatives”, Transportation Research Part A: Policy and Practice 44 (5): pp.291–305.##Mamdoohi, Amir Reza, Mohsen Fallah Zavareh, Christer Hydén, and Trond Nordfjærn (2014) “Comparative analysis of safety performance indicators based on inductive loop detector data”, PROMET  Traffic&Transportation 26 (2): pp.139–49.##National Generation Simulation (NGISM) Programme Home Page (2013) Accessed December 26. http://ngsimcommunity.org/index.php.## Oh, Cheol and Taejin, Kim (2010) “Estimation of rearend crash potential using vehicle trajectory data”, Accident Analysis & Prevention 42 (6): pp.1888–93.##Oh, Cheol, Park, Seri and. Ritchie, Stephen G. (2006) “A method for identifying rearend collision risks using inductive loop detectors.” Accident Analysis & Prevention 38 (2): pp.295–301.##Ozaki, H. [ed.] (1993) “Reaction and anticipation in the carfollowing behavior”, In Transportation and Traffic Theory, Berkeley, California.##Punzo, Vincenzo, Borzacchiello, Maria Teresa and Ciuffo, Biagio (2011) “On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) Program Data”, Transportation Research Part C: Emerging Technologies 19 (6): 1243–62.##Ranjitkar, Prakash, and Nakatsuji, Takashi (2010) “A Trajectory based analysis of drivers’ response in carfollowing situations”, In . http://trid.trb.org/view.aspx?id=910337.##Ratick, S. and G. Schwarz (2009) “Monte Carlo Simulation”, In International Encyclopaedia of Human Geography, edited by Rob Kitchin and Nigel Thrift, pp.175–84. Oxford: Elsevier. http://www.sciencedirect.com/science/article/pii/B9780080449104004764.## Transportation Research Board of the National Academies (2013) “Next Generation Simulation (NGSIM)) core algorithms and data sets”, Accessed December 19. http://trid.trb.org/view.aspx?id=1226819.##Wang, Xuesong and AbdelAty, Mohamed (2006) “Temporal and spatial analyses of rearend crashes at signalized intersections”, Accident Analysis & Prevention 38 (6): pp.1137–50.##Wu, Yihu, Yuan, Huihua , Chen, Haitao and Li, Jigjig (2009) “A study on reaction time distribution of Group Drivers at CarFollowing.” In Second International Conference on Intelligent Computation Technology and Automation, 2009. ICICTA ’09, 3:452–55.##Xu, Cheng, Tarko, Andrew, P., Wang, Wei and Liu, Pan (2013) “Predicting crash likelihood and severity on freeways with realtime loop detector data”, Accident Analysis & Prevention 57 (August): pp.30–39.##Yu, Rongjie and AbdelAty. Mohamed (2014) “Analyzing crash injury severity for a mountainous freeway incorporating realtime traffic and weather data”, Safety Science 63 (March): pp.50–56.##]
1

A Fuzzy BiObjective Mathematical Model for Sustainable Hazmat Transportation
http://www.ijte.ir/article_9607.html
10.22119/ijte.2015.9607
1
Today, transportation of hazardous materials (i.e., hazmat) is a very important issue for researchers due to the risk of this transit, which should be considered for development of industries and transportation. Therefore, a model that is useful should consider the risk and damage to humanitarian and environmental issues due to transit of hazmat materials. By considering the related cost, the reality and applicability of the model are also very important. In this paper, a biobjective model is presented for routing railtruck intermodal terminals with the cost and risk as objective functions by considering three sets of the effective factors that lead to hazardous materials transportation accidents in favor of sustainability. Additionally, a fuzziness concept is considered in the presented model. The model is first validated by the prevalent software using the authoritative solver to solve a numerical example. Furthermore, to help managers, an efficient fuzzy approach proposed in the literature is used. Finally, it is concluded that in this work a reality and sustainable model is suitable for hazmat transportation.
0

231
243


Zohreh
ZahedianTejenaki
Department of Industrial Engineering, University of Tehran
Iran
z.zahedian@ut.ac.ir


Reza
TavakkoliMoghaddam
Department of Industrial Engineering, University of Tehran
Iran
tavakoli@ut.ac.ir
Railtruck intermodal
Hazmat transportation
sustainable
fuzzy multiobjective method
[Agrawal, A. W., Dill, J. and Nixon, H.##(2010) “Green transportation taxes and fees:##A survey of public preferences in California”,##Transportation Research  Part D, Vol. 15, pp.##189–196.##Akgün, V., Parekh, A., Batta, R. and Rump,##C.M. (2007) “Routing of a hazmat truck in the##presence of weather systems”, Computers and##Operations Research, Vol. 34, pp. 1351–1373.##Berman, O., Verter, V. Y. and Kara, B. (2007)##“Designing emergency response networks for##hazardous materials transportation”, Computers##and Operations Research, Vol. 34, pp.##1374–1388.##Bianco, L., Caramia, M. and Giordani, S.##(2009) “A bilevel flow model for hazmat##transportation network design”, Transportation##Research  Part C, Vol. 17, pp. 175–196.##Bonvicini, S. and Spadoni, G. (2008) “A##hazmat multicommodity routing model satisfying##risk criteria: A case study”, Journal of##Loss Prevention in the Process Industries, Vol.##21, pp. 345–358.##Dadkar, Y., Nozick, L. and Jones, D. (2010)##“Optimizing facility use restrictions for the##movement of hazardous materials”, Transportation##Research  Part B, Vol. 44, pp.267–281.##Erkut, E. and Alp, O. (2007) “Designing##a road network for dangerous goods shipments”,##Computers and Operations Research,##Vol. 34, No. 5, pp. 1241–1242.##Erkut, E. and Gzara, F. (2008) “Solving the##hazmat transport network design problem”,##Computers and Operations Research, Vol. 35,##pp. 2234–2247.##Liang, T. F. (2006) “Distribution planning##decisions using interactive fuzzy multiobjective##linear programming”, Fuzzy Sets and##Systems, Vol. 157, pp. 1303–1316.##Lozano, A., Muñoz, A., Macías, L., Pablo##Antún, J., Granados, F. and Guarneros, L.##(2010) “Analysis of hazmat transportation accidents##in congested urbanareas, based on actual##accidents in Mexico”, Procedia Social and##Behavioral Sciences, Vol. 2, pp. 6053–6064.##A Fuzzy BiObjective Mathematical Model for Sustainable Hazmat Transportation##243 International Journal of Transpotation Engineering,##Vol.2, No.3, Winter 2014##Lozano, A., Muñoz, A., Macías, L. and Pablo##Antún, J. (2011) “Hazardous materials transportation##in Mexico City: Chlorine and gasoline##cases”, Transportation Research  Part C,##Vol. 19, pp. 779–789.##Raemdonck, K. V., Macharis, C. and##Mairesse, O. (2013) “Risk analysis system for##the transport of hazardous materials”, Journal##of Safety Research, Vol. 45, pp. 55–63.##Schweitzer, L. (2006) “Environmental justice##and hazmat transport: A spatial analysis in##southern California”, Transportation Research## Part D, Vol. 11, pp. 408–421.##TavakkoliMoghaddam, R., MahmoudSoltani,##F. and Mahmoudabadi, A. (2013) “Bicriteria##hazmat routing problem under uncertainty##– A case study of fuel shipment in##Mazandaran province”, Journal of Transportation##Research, Vol. 4, No. 3, pp. 209–220.##(in Farsi)##TavakkoliMoghaddam, R., Salamatbakhsh,##A., Norouzi, N. and Alinaghian, M. (2011)##“Solving a new vehicle routing problem considering##safety in hazardous materials transportation”,##Journal of Transportation Research,##Vol. 2, No. 3, pp. 223–237. (in Farsi).##Torabi, S. A. and Hassini, E. (2008) “An interactive##possibilistic programming approach##for multiple objective supply chain master##planning”, Fuzzy Sets and Systems, Vol. 159,##pp. 193–214.##Verma, M. and Verter V. (2007) “Railroad##transportation of dangerous goods: Population##exposure to airborne toxins”, Computers and##Operations Research, Vol. 34, pp. 1287–1303.##Verma, M., Verter, V. and Zufferey, N. (2012)##“A biobjective model for planning and managing##railtruck intermodal transportation##of hazardous materials”, Transportation Research## Part E, Vol. 48, pp. 132–149.## World Commission on Environment and Development##(1987), Oxford.## Zhao, L., Wang, X. and Qian Y. (2012)##“Analysis of factors that influence hazardous##material transportation accidents based on##Bayesian networks: A case study in China”,##Safety Science, Vol. 50, pp. 1049–1055.##Zhou, J. (2012) “Sustainable transportation##in the US: A review of proposals, policies, and##programs since 2000”, Frontiers of Architectural##Research, Vol. 1, pp. 150–165.##]
1

Evaluation of Long Term Ageing of Asphalt Mixtures Containing EAF and BOF Steel Slags
http://www.ijte.ir/article_9608.html
10.22119/ijte.2015.9608
1
This study was conducted in order to evaluate the effects of long term ageing on toughness and resilient modulus of asphalt concrete mixtures containing Electric Arc and Basic Oxygen Furnace steel slags. After initial evaluation of the properties of steel slags using Xray Diffraction and Scanning Electric Microscope, eleven sets of laboratory mixtures were prepared. Each set was treated replacing various portions of limestone coarse aggregates of the mixture (≥ 2.36 mm) with steel slags. The main laboratory program consisted of the determination of resilient modulus at three testing temperatures of 5, 20 and 40˚C (ASTM D4123) and indirect tensile strength of the samples at 20˚C. In order to evaluate the long term performance of mixtures containing slags, the specimens were subjected to ageing according to AASHTO PP2 standard method. Results showed that the peak tensile strength, area up to peak tensile strength and total dissipated energy density of the specimens containing Electric arc furnace slag were greater than the control mixtures. Fracture energy was almost the same for both mixes containing basic oxygen furnace slag and limestone. Results also indicated that the resilient modulus of mixtures increased along with an increase in slag contents in asphalt mixtures. The ratio of aged to unaged resilient modulus of the specimens decreased upon increasing slag contents. It was concluded that mixtures containing electric arc furnace slag exhibited less susceptibility to ageing compared with mixtures containing basic oxygen furnace slag and limestone. At 5oC, the highest ratio belonged to control mixtures, which might indicate that at lower temperatures, the susceptibility to ageing of the control mixtures were more pronounced
0

245
265


Seyed Ali
Ziaee
Department of Civil Engineering, Ferdowsi University of Mashhad
Iran
saziaee@um.ac.ir


Amir
Kavussi
Dept of Civil and Environmental Engineering, Tarbiat Modares University
Iran
kavussi@yahoo.co.uk


Morteza
Jalili Qazizadeh
Quchan University of Advanced Technology, Department of Engineering
Iran
morteza.jalili@modares.ac.ir


Abolfazl
Mohammadzadeh Moghadam
Department of Civil Engineering, Ferdowsi University of Mashhad
Iran
EAF and BOF slag
Resilient Modulus
IDT
fracture energy
ageing
[Airey, G. D., Collop A. C. and Thom, N. H.##(2004) “Mechanical performance of asphalt##mixtures incorporating slag and glass secondary##aggregates” Proceedings of the 8th Conference##on Asphalt Pavements for Southern##Africa (CAPSA’04) Sun City, South Africa,##pp.12 – 16.## Anagnos, J. N. Kennedy T.W. (1972) “Practical##method of conducting the indirect tensile##test” Center of Highway Research, University##of Texas at Austin, Research Report 9810,##Austin, Texas.##Asi, I. M., Qasrawi, H. Y. and Shalabi, F. I.##(2007) “Use of steel slag aggregate in asphalt##concretemixes”, Civil Eng.; 34 (8), pp.902–##Aksoy, A., Samlioglu, K., Tayfur, S. and##Ozen, H. (2005) “Effect of various additives##on Moisture damage sensitivity of asphalt##mixtures”, Construction and Building Materials##19, pp 1118.##Bell, C.A. “Summary Report on Ageing of##Asphalt Aggregate Systems”, Project A003A##(Performance Related Testing and Measurement##of AsphaltAggregate Interactions and##Mixtures), Strategic Highway Research Program,##National Research Centre, Washington,##USA, 1989.##Bell, C. A. and Sosnovske, D. (1994) “Ageing:##binder validation”, Strategic Highway##Research Program, National Research Centre,##Washington, USA.##Birgisson, B., Montepara, A., Romeo, E.,##Roque, R., Roncella, R. and Tebaldi, G. (2007)##“Determination of fundamental tensile failure##limits of mixtures” J AssoccAsphalt Paving##Technol;76:303–44.##EsmaeiliKalalagh, A, Marandi, S. M. and##Safapour, P. (2005)“Technical effects of air##Seyed Ali Ziaee, Amir Kavussi, Morteza Jalili Qazizadeh, Abolfazl Mohammadzadeh Moghadam##International Journal of Transpotation Engineering, 264##Vol.2, No.3, Winter 2014##cooled blast furnace slag on asphalt mixtures”##Transportation Research Center.##Hamzah, M.O., and Teoh C. Yi. (2008) “Effects##of Temperature on Resilient Modulus of##Dense Asphalt Mixtures Incorporating Steel##Slag Subjected to Short Term Oven Ageing”,##World Academy of Science, Engineering and##Technology, 22.##Huang, Y. H. (2004) “Pavement analysis and##design”, Prentice Hall, New Jersey.##Kavussi, A. and Modarres, A. (2010) “Laboratory##fatigue models for recycled mixes with##bitumen emulsion and cement”, Construction##and Building Materials 24; pp.1920–1927.##Lee, H. J. (1996)“Uniaxial constitutive modeling##of asphalt concrete using viscoelasticity##and continuum damage theory” Ph.D. dissertation.##Raleigh: North Carolina State University.##Li, Q., Jong, Lee H. and Kim, T. W. (2012) ”A##simple fatigue performance model of asphalt##mixtures based on fracture energy”, Construction##and Building Materials, Volume 27, Issue##1, Pages 605611.##Liz, Hunt P.E and Glenn, E. (2000) “Steel##slag in hot mix asphalt concrete” State Research##Project #511 Oregon Department of##Transportation, Salem, Oregon.##Moreno, F. and Rubio, M.C. (2013) “Effect##of aggregate nature on the fatiguecracking##behavior of asphalt mixes” Materials and Design##47; pp.61–67.##Pasetto, M. and Baldo, N. (2010) “Experimental##evaluation of high performance base##course and road base asphalt concrete with##electric arc furnace steel slags” Journal of##Hazardous Materials 181 938–948.##Pasetto, M. and Baldo, N. (2011) “Mix design##and performance analysis of asphalt concretes##with electric arc furnace slag” Construction##and Building Materials; 25: pp.3458–3468.##Petersen, J. C. (2002) “Chemistry of asphaltaggregate##interaction” Laramie, Wyoming:##Moisture damage symposium.##Roque, R., Birgisson B., Sangpetngam, B. and##Zhang, Z.W. (2002) “Hot mix asphalt fracture##mechanics: a fundamental crack growth law##for asphalt mixtures” J. Assoc Asphalt Paving##Technol;71:pp.816–27.##Shaopeng, W., Yongjie, X. and Qunshan, Y.##(2007) “Utilization of steel slag as aggregates##for stone mastic asphalt (SMA) mixtures”,##Building and Environment, 42: pp.2580–##Sofilić, T., RastovčanMioč, A.,Ćosić, M.,##Merle, V.,Mioč, B. and Sofilić, U. (2010)##“EAF steel slag application possibilities in##Croatian asphalt mixture production” Chemical##Engineering Transactions, VOL 19.##Waligora, J., Bulteel. D., Degrugilliers. P. and##Damidot, D. (2010) “Chemical and mineralogical##characterizations of LD converter steel##slags: A multianalytical technique approach”##Materials Characterization, 61: pp.39 – 48.##Xie, J., Chen, J., Shaopeng, W., Lin, J. and##Wei, W. (2013) “Performance characteristics##of asphalt mixture with basic oxygen furnace##slag”, Construction and Building Materials##38, pp.796–803.##Evaluation of Long Term Ageing of Asphalt Mixtures Containing EAF and BOF Steel Slags##265 International Journal of Transpotation Engineering,##Vol.2, No.3, Winter 2014##Yongjie, X., Shaopeng, W., Haobo, H. and##Jin, Z. (2006)“Experimental investigation of##basic oxygen furnace slag used as aggregate##in asphalt mixture” Journal of Hazardous Materials,##B138: pp.261–268.##You, Z. and Buttlar, W. (2004) ”Discrete##element modeling to predict the modulus of##asphalt concrete mixtures.” J. Mater. Civ.##Eng.16, Special Issue: Micromechanical##Characterization and Constitutive Modeling##of Asphalt Mixes, pp.140–146.##]