Improved online sequential extreme learning machine for identifying crack behavior in concrete dam. Issue 2 (January 2019)

• Record Type: Journal Article
• Authors: Dai, Bo; Gu, Chongshi; Zhao, Erfeng; Zhu, Kai; Cao, Wenhan; Qin, Xiangnan
• Is Part Of: Advances in structural engineering. Volume 22:Issue 2(2019)
• Page Start: 402
• Page End: 412
• Publication Date: 2019-01
• Subjects: bootstrap confidence intervals -- crack behavior -- genetic algorithm -- identification model -- online sequential extreme learning machine; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433218788635
• Languages: English
• ISSNs: 1369-4332

Abstract

Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BC a method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior.