MAINTENANCE METHODOLOGIES OF OLD RAILWAY TUNNELS
Tunnel management and maintenance are important issues in todays transportation infrastructure system. Tunnel management systems help engineers and inspectors organize and analyze data collected about tunnels. The results of this data can then be evaluated and used to predict future tunnel deterioration patterns and corresponding maintenance needs. These predictions and decisions are made under uncertainty. A knowledge based system (KBS) named MATUF, is an example of such a system. The traditional knowledge based systems, the so-called ruled based systems, suffer from significant deficiencies handling decision making under uncertainty. Bayesian Networks (BN) are more suitable to handle uncertainty in this context. They were developed as a decision support tool originally used for purposes of artificial intelligence (AI) engineering. The goal of this paper is to present the potential of the application of AI techniques to managing and maintenance of old railway tunnels.
LUIS RIBEIRO E SOUSA RITA LEAL E SOUSA CRISTINA SILVA VITOR FREITAS
University of Porto, Dep.of Civil Engineering, Portugal Massassuchets Institute of Technology, Cambridge, USA Polytechnical Scholl of Porto, Porto, Portugal REFER, Esta(c)(a)o de Santa Apol(o)nia, Lisboa, Portugal
国际会议
三亚
英文
401-406
2009-05-24（万方平台首次上网日期，不代表论文的发表时间）