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Big Data Predictive Modelling for Infrastructure Asset Management
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2018 |
| Abstract | In the last few years predictive analytics has become a mainstream topic. Companies and organizations of every size and in every industry are looking at the big data they have collected and maintained to see if they can use it to make predictions that will help them be more effective, more customer-centric and more profitable. In infrastructure asset management, predictive modelling refers to the activity that patterns and predicts the deterioration of infrastructure asset condition with accumulating use, based on comprehensive evaluation of the structural and functional characteristics of the asset in service. Being able to predict the condition of the asset is the most essential activity to the operation and maintenance management of infrastructure at both the network and the project levels and these models play a crucial role in several aspects of the infrastructure asset management, including financial planning and budgeting. However, performance modelling is the most technologically difficult portion of infrastructure asset management due to the uncertainties of the asset behaviour under a number of influencing factors including their designs and constructions, their operating environment and other factors. This paper introduces the pioneer predictive modelling work we have carried out for our clients with their big infrastructure asset management data and advanced analytics approaches and demonstrates how these approaches and predictive models help them make better investment decisions in managing their infrastructure assets. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://road.cnki.net/download/jtdh/1802200521380001.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
