title: Risk assessment of sewer condition using artificial intelligence tools: application to the SANEST sewer system.
abstract: Operation, maintenance and rehabilitation comprise the main concerns of wastewater infrastructure asset management. Given the nature of the service provided by a wastewater system and the characteristics of the supporting infrastructure, technical issues are relevant to support asset management decisions. In particular, in densely urbanized areas served by large, complex and aging sewer networks, the sustainability of the infrastructures largely depends on the implementation of an efficient asset management system. The efficiency of such a system may be enhanced with technical decision support tools. This paper describes the role of artificial intelligence tools such as artificial neural networks and support vector machines for assisting the planning of operation and maintenance activities of wastewater infrastructures. A case study of the application of this type of tool to the wastewater infrastructures of Sistema de Saneamento da Costa do Estoril is presented.
authors.affiliation: Department of Civil Engineering, Architecture and GeoResources, Technical University of Lisbon - IST, Av. Rovisco Pais 1049-001 Lisbon, Portugal E-mail: vitor.sousa@ist.utl.pt.
authors.forename: V
authors.initials: V
authors.lastname: Sousa
authors.affiliation: Department of Civil Engineering, Architecture and GeoResources, Technical University of Lisbon - IST, Av. Rovisco Pais 1049-001 Lisbon, Portugal E-mail: vitor.sousa@ist.utl.pt.
authors.forename: J P
authors.initials: JP
authors.lastname: Matos
authors.affiliation: Department of Civil Engineering, Architecture and GeoResources, Technical University of Lisbon - IST, Av. Rovisco Pais 1049-001 Lisbon, Portugal E-mail: vitor.sousa@ist.utl.pt.
authors.forename: N
authors.initials: N
authors.lastname: Almeida
authors.affiliation: Department of Civil Engineering, Architecture and GeoResources, Technical University of Lisbon - IST, Av. Rovisco Pais 1049-001 Lisbon, Portugal E-mail: vitor.sousa@ist.utl.pt.
authors.forename: J
authors.initials: J
authors.lastname: Saldanha Matos
mesh_terms: D016002:Discriminant Analysis; D004323:Drainage, Sanitary; D016571:Neural Networks, Computer; D011174:Portugal; D018570:Risk Assessment; D060388:Support Vector Machine