In recent years, the construction of prefabricated underground stations (PUS) has become an important aspect of low-carbon urban development. At present, the design of PUS assembled joints involving design-fabrication...
详细信息
In recent years, the construction of prefabricated underground stations (PUS) has become an important aspect of low-carbon urban development. At present, the design of PUS assembled joints involving design-fabricationtransportation-assembly industry chain information encounters technical bottleneck. To address this problem, this study proposed a big data decision-making model for PUS assembled joints considering multi-source massive data. First, the databases of assembled joints, precast components and assembled structures were established. Second, the finite element analysis analyzer linked to the databases was built. Third, the structure optimization algorithm and joint decision-making algorithm were developed. Finally, the Data-driven Adaptive Decisionmaking Model (DADM) was constructed by combining the above results. This study used DADM for case study and compared with the empirical design, the main conclusions were as follows: (1) DADM revealed the adaptability of joint properties in PUS, including the joint properties influence and the joint type decision. (2) DADM performed joint-component-structure planning more accurately than empirical design. This compensates the disadvantage of empirical decision making under massive data. (3) DADM presented significant advantages of semi-rigid joints for PUS applications. And DADM also achieved economical planning that precisely matched the joint performance requirements. (4) The advantages of the adaptive scheme were comprehensive, including good economics and industry chain benefits and improved station quality. This study addresses difficulty of designing PUS assembled joints with multi-source massive data, which has important application value and practical significance.
暂无评论