Utilizing Architecture Design to Solve Structural Engineering Problems with Deep Learning

Authors

  • Luxmi Sapra, Ankit Mathani, Gunjan Bhatnagar

DOI:

https://doi.org/10.17762/msea.v71i4.685

Abstract

Before, when there was an existing construction solution service-related project, it would only provide a limited selection of construction services, and contracts would be managed and documented manually. Some applications have caused a noteworthy amount of delays in the huge construction project, and these delays have influenced on the execution of strategic planning. The lack of cash flow and financial difficulties experienced by customers, poor site management by contractors, insufficient contractor experience, a shortage of site workers, and ineffective planning and scheduling on the part of contractors were among the major disadvantages that contributed to a lack of customer trust in this industry.In order to circumvent this obstacle, ArCDEx set a mission to simplify the process of doing business for our customers by providing the whole construction in a single package at competitive prices. From the very beginning to the very end, the goal of this project is to provide a professional service that makes use of expertise, project management planning, and designing. It has a staff of architects, structural engineers, landscape designers, consultants, and professionals who provide services connected to construction. It also provides features and functionalities for managing construction works from the project's inception all the way through to its completion. These features include the ability to monitor the various stages of the construction process, such as the procurement of design services and contractors, construction methods, and the management of the construction process.

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Published

2022-09-02

How to Cite

Luxmi Sapra, Ankit Mathani, Gunjan Bhatnagar. (2022). Utilizing Architecture Design to Solve Structural Engineering Problems with Deep Learning. Mathematical Statistician and Engineering Applications, 71(4), 1605–1611. https://doi.org/10.17762/msea.v71i4.685

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Section

Articles