A Mathematical methodology for the preventive study of the failure rate to optimize the Program Maintenance of a public work: economic-management aspects for safety and quality
DOI:
https://doi.org/10.30564/jbar.v1i1.138Abstract
In this paper we analyze the problem of the assessment of the failure rate of the complex public work system and the engineering part of it (bridge, tunnel, etc.), examining the case of serious maintenance problems, such as those which occurred in the recent disaster of the "Morandi bridge".
The original mathematical methodology envisaged makes it possible to optimize the safety and quality scenarios of the operation and infrastructure in question, also from an economic-management point of view, evaluating every aspect in an integrated way and for the entire lifespan.
The scientific results obtained are of particular interest for the study of maximization of the planning protocols of "terotechnological" interventions, providing a contribution to the science of programmed maintenance for the mobility networks and for more complex parts such as bridges and tunnels.References
[1] Aalen, O.O. (1976) - Statistical Theory for a family of Counting Processes, Institute of Mathematical Statistics, University of Copenhagen.
[2] Alexandrov N.M, Lewis R.M., An Overview of First-Order Model Management for Engineering Optimization, Optimization and Engineering, 2001.
[3] Andrade C. & Co. Management, maintenance and strengthning of concrete structures. Fib (CEB- FIP) Bulletin 17, 2002.
[4] Appleton J. & Co. Strategies for testing and assessment of concrete structures. CEB Bulletin d’information n°243, May 1998.
[5] Bigano A. (2008), Teoria delle scelte razionali in condizioni di incertezza, Pubbl. Fac. scienze statistiche ed economiche, Università Bicocca, Milano.
[6] Burke, M.D., Csörgö, S. and Horvat, L. (1981) - Strong approximations of some biometric estimates under random censorship, Z. Wahrscheinlichkeit. verw. Gebiete 56 87-112.
[7] Burke, M.D., Csörgö, S. and Horvat, L. (1988) - A correction to and an improvement of "Strong approximations of some biometric estimates under random censorship, Probability Theory and Related Fields, 79, 51-57.
[8] Chung, K.L. (1949) - An estimate concerning the Kolmogorov limit distribution, Trans. Amer. Math. Soc., 67, 36-50.
[9] Csörgö, S. and Horvat, L. (1983) - The rate of strong uniform consistency far the product-limit estimator, Z. Wahrscheinlichkeit. verw. Gebiete, 61, 411-426.
[10] Einmahl, J.H.J. and Konig, A.J. (1992) - Limit theorems for a generaI weighted process under random censoring, The Canadian Journal of Statistics, 20, 77-89.
[11] Finkelstein, H. (1971) - The law of the iterated logarithm for empirical distributions, Ann. Math. Statist., 42, 607-615.
[12] Földes, A., Rejtö, L. and Winter, B.B. (1981) - Strong consistency prop- erties of non-parametric estimators for randomly censored data. II:Estimation of density and failure rate. Period Math. Hung, 12, 15-29.
[13] Gill, R.D. (1980) - Censoring and Stochastic Integrals, Math. Centr. Tracts, 124, Mathematisch Centrum, Amsterdam.
[14] Gu, M.G. and Lai, T.L. (1990) – Functional laws of the iterad logarithm for the product-limit estimator of a distribution function under random censorship or truncation, Ann. Probab., 18, 160-189.
[15] Haftka R., Combining Global and Local Approximation, AIAA Journal vol.29, 1999.
[16] Hall, P. (1981) - Laws of iterad logarithm for nonparametric density estimators, Z. Wahrscheinlichkeit. verw. Gebiete, 56, 47-61.
[17] Kahneman D., Knestch J.L. (1992), Valuing public goods: the purchase of moral satisfaction. Journal of Environmental Economics and Management.
[18] Kalbfleisch, J.G. and Prentice, R.L. (1980) - The Statistical Analysis of Failure Time Data, Wiley, New York.
[19] Kamrad B. e Ritchen P. (1991), Multinomial approximating models for options with k state variables, Management Science, vol. 37, 1640-1652.
[20] Kanninen B.J. (1993), Optimal experimental design for double-bounded dichotomous choice contingent valuation. Land Economics.
[21] Kaplan, E.L. and Meier, P. (1958) - Nonparametric estimation from incomplete observations, J. Amer. Statist. Assoc., 53,457-481.
[22] Levine M., Stephan D., Szabat K. (2014), Statistics for managers using Microsoft Excel, Pearson Education.
[23] Liu, R. Y.C. and Van Ryzin, J (1985) - A histogram estimator of the hazard rate with censored data, Ann. Statist., 13, 592-605.
[24] Lo, S.H., Mack, Y.P. and Wang, J.L. (1989) - Density and hazard rate estimation far censored data via strong representation of the Kaplan-Meier estimator, Prob. Theor. Related Fields, 80, 461-473.
[25] Lo, S.H. and Singh, K (1986) - The product-limit estimatore and the bootstrap: Some asymptotic representations, Probab. Theor. Related Fields, 71, 455-465.
[26] Mason, D.M. (1985) - A strong invariance principle for the tail empirical process, Ann. Inst. Henri Poicaré, Probab. Statist., 24,491-506.
[27] Mun J. (2006), Real options analysis : tools and techniques for valuing strategic investment and decisions, Wiley finance series, Hoboken.
[28] Pellegrino C., Pipinato A., Modena C., A simplified management procedure for bridge network maintenance. Vol.7, No.5, 341-351, May 2011.
[29] Rose S. (1998), Valuation of interacting real options in a tollroad infrastructure project, The Quesrterly Review of Economics and Finance, vol. 38, special issue, p. 711- 723.
[30] Rothengatter, W., (1994), Do external benefits compensate for external costs of transport?, Transportation Research, 28A (4) pp. 321-328.
[31] Schäfer, H (1986) – Local convergence of empirical measures in the random censorship situation with application to density and rate estimators. Ann. Statist., 14, 1240-1245.
[32] Tanner, M and Wong, W. (1989) – The estimation of the hazard function from randomly censored data by the kernel method, Ann. Statist., 11, 989-993.
[33] Wankhade M. W. and Kambekar A. R. (2013). Prediction of Compressive Strength of Concrete using Artificial Neural Network.
[34] Watson, G.S. and Leadbetter, M.R. (1964a) – Hazard analysis, I Biometrika, 51, 175-184.
[35] Watson, G.S. and Leadbetter, M.R. (1964b) – Hazard analysis, II Sankhya Ser. A, 26, 101-116.
[36] Yandell, B.S. (1983) – Nonparametric inference fro rates with censored survival data, Ann. Statist., 11, 1119-1135
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