Lingling Wang
ABSTRACT
Highway projects are usually built in extremely complex natural and cultural environments, which is a continuous and dynamic management practice process. Therefore, it often has the characteristics of long construction period, large investment scale, many security risks and high technical requirements. How to effectively reduce these uncertain risk factors is the direction of continuous progress of relevant researchers.
At present, domestic and foreign scholars have carried out relevant research on the evaluation method of highway engineering investment risk, and achieved certain research results. However, the research in this field has the characteristics of extensive conceptual extension, overlapping terminology meaning and inconsistent paradigm. The traditional methods of highway engineering investment risk assessment include expert case analysis, empirical analysis and other traditional methods. These evaluation methods are extensive, biased towards macro, subjective factors have great influence, lack of intelligent means, low efficiency, low accuracy, and do not form a systematic evaluation system.
Therefore, this paper proposes to use the broad learning system to evaluate the investment risk of highway projects. Taking the actual investment risk data of highway projects in the Second Public Administration of China Communications Group as the research object, and using the method of combining experience and professional tools, we first establish a detailed highway risk evaluation index system, and then establish the broad learning system model without incremental learning and with incremental learning. The actual project data are used as the data source for training, and the intelligent evaluation results of the two are compared.
The experimental results show that the prediction accuracy of the broad learning system containing incremental learning has been significantly improved, which can provide investment risk decision support services for highway investment projects in China, and has important reference significance.
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Index Terms
- Risk Assessment of Highway Engineering Investment Based on Broad Learning System
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