Research On Dynamic Optimization Of Construction Progress Of Engineering Projects Considering Robustness

With the development of building construction informatization and intelligent technology,A large amount of data information can be sensed and recorded during the construction process which provides an important basis and reference for project decision-making and management.At the same time,the internal and external environment of the project is becoming more and more complex,which makes various uncertain factors in the construction process also increase with the complexity of the environment.Therefore,how to use intelligent technologies and methods to optimize the schedule with strong anti-interference ability during the construction process is of great significance to ensure that the project is completed on schedule and on time.This article aims to utilize the project data information recorded in the smart construction site platform to build a activity completion rate prediction model and a construction progress robust dynamic optimization model,robust optimization of base month schedules during construction,thereby enhancing the anti-interference ability of the schedule and ensuring the smooth implementation of the project as planned.This article first summarizes and analyzes the existing literature on progress forecasting and progress optimization,and then introduces the refined management,smart construction site,robust optimization concepts and model-related theories applied in this paper;Secondly,the data information in the smart construction site platform is compared with the progress risk factors induced by the literature research method,the input variables of the model are screened out,and a prediction model of activity completion rate based on the improved gray wolf algorithm optimization extreme learning machine(IGWO-ELM)is constructed,and the validity and adaptability of the model are verified by taking an actual project as an example;On this basis,the progress deviation of each activity is analyzed based on the prediction data of activity completion rate,and this is used as the basis for judging whether robust optimization is required.Then,aiming at the goal of maximizing the robustness of the schedule,a robust dynamic optimization model of the construction schedule is constructed and a particle swarm algorithm is designed to solve the model;finally,through the analysis of engineering examples,it is verified that the robust schedule generated by the method in this paper can effectively "absorb"the activity delay caused by the uncertain factors in the project implementation process,and compared to the baseline plan,it has high anti-interference ability and stability..The robust construction schedule dynamic optimization method proposed in this article makes reasonable use of the data information in the smart construction site platform,and can generate a robust schedule plan during the construction process,which helps to realize intelligent and refined management of construction progress,and ensure that projects are completed on schedule and on time.