The paper deals with the life-cycle performance prediction of deteriorating Reinforced Concrete (RC) structures by means of Artificial Neural Networks (ANNs). A three-layer ANN is developed and trained to capture the overall system performance based on limited amount of information related to local damage of some components, typically obtained from the results of visual inspections. The training datasets are formed to incorporate the results from several inspections carried out over given observation time intervals and to accommodate predictions over the remaining structural lifetime. The proposed ANN is applied to the life-cycle seismic capacity assessment of a three-story RC frame under chloride-induced corrosion.

Life-cycle seismic performance prediction of deteriorating RC structures using artificial neural networks

Bianchi S.;Biondini F.
2018-01-01

Abstract

The paper deals with the life-cycle performance prediction of deteriorating Reinforced Concrete (RC) structures by means of Artificial Neural Networks (ANNs). A three-layer ANN is developed and trained to capture the overall system performance based on limited amount of information related to local damage of some components, typically obtained from the results of visual inspections. The training datasets are formed to incorporate the results from several inspections carried out over given observation time intervals and to accommodate predictions over the remaining structural lifetime. The proposed ANN is applied to the life-cycle seismic capacity assessment of a three-story RC frame under chloride-induced corrosion.
Life-Cycle Analysis and Assessment in Civil Engineering – Towards an Integrated Vision
9781138626331
9781315228914
File in questo prodotto:
File Dimensione Formato  
IALCCE2018_2.pdf

Accesso riservato

Descrizione: 2018_IALCCE_2
: Pre-Print (o Pre-Refereeing)
Dimensione 984.42 kB
Formato Adobe PDF
984.42 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1074710
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact