Risk-based Inspection on Highway Assets with Category 2 Defects

Risk-based Inspection on Highway Assets with Category 2 Defects

E.O. Ekpiwhre K.F. Tee S.A. Aghagba K. Bishop 

Department of Engineering Science, University of Greenwich, UK

Department of Highways, Ministry of Works, Delta State, Nigeria

Asset Management Directorate, Transport for London, UK

Page: 
372-382
|
DOI: 
https://doi.org/10.2495/SAFE-V6-N2-372-382
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 June 2016
| Citation

OPEN ACCESS

Abstract: 

This paper presents a study piloted on highway assets Category 2 defects. Imminent hazards on high-way road networks are significantly accelerated by structural deterioration of highway infrastructures. The inspection and maintenance strategy for highway infrastructure requires continuous improvements to reduce high occurrence of defective highway assets. Combined risk-based inspection (RBI) and stochastic (STOC) techniques is considered in this investigation to give an in-depth understanding of highway asset maintenance response. Appropriate data information is extracted from Network Maintenance Management System and complementary information elicited from maintenance experts as well as recommended standards. Safety inspections piloted within the period of 5 years is evaluated using the projected RBI-STOC approach. The RBI incorporates the consequences and likelihood of the defects and the combined STOC techniques utilised defines the actual maintenance interval operated. The RBI-STOC approach proposes reclassification of highway asset defect repair intervals, appropriate maintenance task response and efficient maintenance prioritisation of highway assets in equivalence with contribution to system average mean time to repair and downtime.

Keywords: 

category 2 defects, probability distribution, risk-based highway assets management, risk-based inspections, safety inspection

  References

[1] Transport for London (Tfl), Tfl Good Practice Guide: Risk-Based Inspection of Highway Structures, 2011.

[2] Tee, K.F. & Li, C.Q., A numerical study of maintenance strategy for concrete structures in marine environment. Proceeding of the 11th International Conference on Applications of Statistics and Probability in Civil Engineering, Zurich, Switzerland, pp. 618–625, 2011. http://dx.doi.org/10.1201/b11332-94

[3] Basset, G., Highways Risk Management Review, 2011.

[4] Adurokiya, E., 23 Months After, Multi-Million Naira Enerhen Junction Stinks. Nigeria Tribune, 2015.

[5] Ekpiwhre, E.O. & Tee, K.F., Reliability-centered maintenance of road junction transport assets. Proceeding of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing, eds. J. Kruis, JY. Tsompanakis & B.H.V. Topping, Civil-Comp Press, pp. 279–289, 2015. http://dx.doi.org/10.4203/ccp.108.279

[6] Department for Transport (DfT), Well-maintained Highways: Code of Practice for Highway Maintenance Management, The Stationary Office, London, 2005.

[7] Khan, F.I., Sadiq, R. & Haddara, M.M., Risk-based inspection & maintenance (RBIM): multi-attribute decision-making with aggregative risk analysis. Process Safety and Environmental Protection, 82(6), pp. 398–411, 2004. http://dx.doi.org/10.1205/psep.82.6.398.53209

[8] Washer, G., Connor, M., Nasrollahi, M. & Provines, J., New framework for risk-based inspection of highway bridges. Journal of Bridge Engineering, 21, pp. 1–8, 2016. http://dx.doi.org/10.1061/(asce)be.1943-5592.0000818

[9] Orugbo, E.E., Alkali, B., DeSilva, A. & Harrison, D., Reliability analysis of trunk road network maintenance: a study of category 1 defects. Advances in Manufacturing Technology XXVI, eds. T.S. Baines, B. Clegg & D.K. Harrison., pp. 115–120, 2011.

[10] Tee, K.F. & Khan, L.R., Risk-cost optimization and reliability analysis of underground pipelines. Proceeding of the 6th International ASRANet Conference, London, UK, 2012.

[11] Dicdican, R.Y., Haimes, Y.Y. & Lambert, J.H., Risk-based asset management methodology for highway infrastructure systems, FHWA/VTRC 04-CR1, pp. 451–467, 2004.

[12] America Petroleum Institute (API). Risk-Based Inspection, API Recommended Practice 580, 2009.

[13] Washer, G., Connor, M., Nasrollahi, M. & Reising, R., Verification of the framework for risk-based bridge inspection. Journal of Bridge Engineering, 21(4), 04015078, 2016. http://dx.doi.org/10.1061/(ASCE)BE.1943-5592.0000787

[14] Wintle, J.B., Kenzie, B.W., Amphlett, G.J. & Smalley, S., Best Practice for Risk-based Inspection as a Part of Plant Integrity Management, Health & Safety Executive (HSE) Books, 2001.

[15] Mahmoodian, M., Alani, A.M. & Tee, K.F., Stochastic failure analysis of the gusset plates in the Mississippi river bridge. International Journal of Forensic Engineering, 1(2), pp. 153–166, 2012. http://dx.doi.org/10.1504/IJFE.2012.050415

[16] Tee, K.F., Khan, L.R., Chen, H.P. & Alani, A.M., Reliability based life cycle cost optimization for underground pipeline networks. Tunnelling and Underground Space Technology, 43, pp. 32–40, 2014. http://dx.doi.org/10.1016/j.tust.2014.04.007

[17] Khan, L.R., Tee, K.F. & Alani, A.M., Reliability-based management of underground pipeline network using genetic algorithm. Proceeding of the 11th International Probabilistic Workshop, Brno, Czech Republic, pp. 159–170, 2013.

[18] Fang, Y., Chen, J. & Tee, K.F., Analysis of structural dynamic reliability based on the probability density evolution method. Structural Engineering and Mechanics, 45(2), pp. 201–209, 2013. http://dx.doi.org/10.12989/sem.2013.45.2.201

[19] Fang, Y., Wen, L. & Tee, K.F., Reliability analysis of repairable k-out-n system from time response under several times stochastic shocks. Smart Structures and Systems, 14(4), pp. 559–567, 2014. http://dx.doi.org/10.12989/sss.2014.14.4.559

[20] Marhavilas, P.K. & Koulouriotis, D.E., The Deterministic and Stochastic Risk Assessment Techniques in the Work Sites: A FTA-TRF Case Study, INTECH Open Access Publisher, 2012.

[21] Limnios, N., Fault Trees, John Wiley & Sons, 2013.