title: Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.
abstract: The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.
authors.affiliation: DICEA, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy E-mail: roberta.padulano@unina.it.
authors.forename: G
authors.initials: G
authors.lastname: Del Giudice
authors.affiliation: DICEA, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy E-mail: roberta.padulano@unina.it.
authors.forename: R
authors.initials: R
authors.lastname: Padulano
authors.affiliation: DICEA, Università degli Studi di Napoli Federico II, 80125 Napoli, Italy E-mail: roberta.padulano@unina.it.
authors.forename: D
authors.initials: D
authors.lastname: Siciliano
mesh_terms: D002947:Cities; D015999:Multivariate Analysis; D011336:Probability; D012722:Sewage / Q000032:analysis*; D018505:Waste Management / Q000379:methods / Q000706:statistics & numerical data*
chemical_list: D012722:Sewage