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The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Lu, S.-L. (2000) The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network. In: 17th IAARC/CIB/IEEE/IFAC/IFR International Symposium on Automation and Robotics in Construction (IAARC 2000), 18-20 Sep 2000, Taipei, Taiwan, pp. 1-8.

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Official URL: http://www.iaarc.org/publications/proceedings_of_t...

Abstract/Summary

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Construction Management and Engineering > Business Innovation in Construction
Faculty of Science > School of Construction Management and Engineering > Digital Practices
ID Code:20546

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