Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Authors:
Min-Yuan Cheng;Andreas F. V. Roy;Kuan-Lin Chen
Affiliations:
Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC;Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC and Department of Civil Engineering, Parahyangan Cat ...;Department of Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Road slope collapse events are frequent occurrences in Taiwan, often exacerbated by earthquakes and/or heavy rainfall. Such collapses disrupt transportation, damage infrastructure and property, and may cause injuries and fatalities. While significant efforts are regularly invested in reducing road slope collapse risk, most focus exclusively on limiting the potential for slope failure. Collapse prediction efforts may result in inference errors that cause allocated road slope maintenance resources to be expended inefficiently, resulting in relatively higher collapse risk than should be achievable under ideal circumstances. Most maintenance programs rely on decision maker risk preferences, as his/her knowledge and experience can contribute to risk assessment decision making. The decision maker is capable of choosing an acceptable balance between two types of inference error, i.e., @a and @b errors. This preference may later be used as guidance to minimize inference error. This paper proposed the evolutionary risk preference fuzzy support vector machine inference model (ERP-FSIM) as a hybrid AI system able to make predictions regarding road slope collapse that takes decision maker risk preference into account. Validation results demonstrate ERP-FSIM viability, as level of average error both for the training set and validation set conform to the decision maker risk preference ratio and is significantly lower than the error tolerance of +/-10%.