## 5 Conclusions  
(1) Considering the time value of capital and the rewards and punishments for the construction time, a quadratic curve is used to represent the relationship between the direct cost and the construction time of each work, and the relationship between the construction time and cost function is determined. Taking the net present value of costs as the economic evaluation indicator, the direct costs are paid once at the intermediate time points of each work, the indirect costs are calculated once at the end of each interest period, and the project rewards and punishments are calculated once at the end of the project period. A dynamic optimization model of time-cost for deep foundation pits is established.  
(2) BIM technology can quickly and accurately obtain project information, and genetic algorithms can search globally. By combining BIM technology with the genetic algorithm, the time-cost optimization process is established. By adopting a flow construction method, the construction of deep and large foundation pits in the project is decomposed into 27 works, and the project parameters are reasonably determined. The genetic parameters L, P, G, P\({}_{c}\), and P\({}_{m}\) are 27, 400, 1200, 0.5, and 0.0003, respectively.  
(3) The dynamic optimization scheme reduces the total cost by 1.68% while reducing the total construction time by 8.47%. The static optimization scheme reduces the total cost by 0.31% while reducing the total construction time by 10.17%. The dynamic optimization scheme extends the construction period by 2 days while reducing the total cost by 89,500 yuan compared to static optimization. Dynamic optimization considers the time value of funds, which is more in line with engineering reality and has greater guidance value for the project.  
(4) Visual construction simulation is conducted on the dynamic optimization scheme using the BIMSD platform, and combined with the funding demand curve, the feasibility of the scheme is verified from both qualitative and quantitative perspectives. The peak value of the fund demand curve before and after optimization has not changed much, reaching 128,000 yuan and 127,000 yuan, respectively. The various resources and funding supply of the project can meet the needs of optimizing the operation of the plan. The uneven coefficient of fund demand has changed from 2.08 to 2.16, and the number of key routes has changed from one to multiple, making construction management more difficult. During the implementation of the project, real-time monitoring of the project progress is carried out using the BIMSD platform, and any deviations are promptly adjusted.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]); software, [PERSON]; validation, H.G.; resources, [PERSON] ([PERSON]).; data curation, [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and H.G.; project administration, [PERSON] ([PERSON]); All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research were funded by National Natural Science Foundation of China and the Key Scientific Research Project of China Railway 19 th Bureau Group Co., Ltd., grant number U1604135 and 19-A04.  
**Institutional Review Board Statement:** The research does not require ethical approval.  
**Informed Consent Statement:** Not applicable. The research did not involve humans.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** We thank all authors for their assistance with this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.