In addition, simulation models represent the: (1) overall logic of the multitude of activities required to construct a building (2) resources involved in performing the work (e.g., labor, equipment, management, etc.) and (3) environment in which the project is being built (e.g., site conditions, labor pool, market situation, etc.). Such simulations can be utilized to quantify and validate the efficiency of the construction process. ![]() However, computer simulations can be used to improve a plan’s reliability, by explicitly incorporating performance factors and mutual causal relationships. Existing planning and control techniques are insufficient for predicting reliable and adequate on-site performance. However, “construction companies still lack the ability to properly plan, estimate, and execute projects in a consistent, efficient, and reliable manner”. Reliable predictions of productivity at the operational level minimize uncertainties, facilitate efficiency, and decrease waste in terms of time, cost, and materials. ![]() Such complexity adds unpredictability, and prudent planning is required. Finally, construction projects operate under numerous constraints, in dynamic environments, and require the coordination of multiple tasks. Moreover, since a construction project involves numerous stakeholders (including owners, designers, contractors, and the government), coordination of all of the relevant participants can be challenging. Construction work involves multiple processes and the complex interactions of a wide variety of components that are connected by nonlinear relationships. Also, a variety of operational and managerial factors influence productivity. This persistent newness results in frequent modifications to the planned schedule. The newness of the construction environment also adds unknowns to the execution process, so that planning based on historical data cannot guarantee the expected performance. For instance, developments differ in location, design, level of skilled labor required, and team composition. First, projects include numerous risks and uncertainty. Each construction project is unique and complex. Since productivity on construction sites is dynamic, it is challenging to develop sufficiently reliable construction plans. Productivity dynamics can proliferate such variations between planned and actual production, resulting in an exacerbation of on-site problems. However, since construction projects are inherently dynamic and complex, the work productivity on site can vary daily, according to the type and number of mitigating factors. Thus, reliable planning is extremely important to managerial efficiency and waste reduction. ![]() On the other hand, if the actual performance is lower than expected, materials become excessive construction costs then increase due to interest accrual and inventory management. If the actual performance is greater than expected, materials become scarce and labor and equipment are wasted. Such differences lead to the supply of materials not coinciding with demand on the construction site construction managers must then wrestle with an excess or lack of materials. This variability between planned and actual performance results in managerial inefficiency and, ultimately, lower-quality outcomes. Our results show that the developed framework facilitates the reliable prediction of productivity dynamics, and can contribute to improved schedule reliability, optimized resource allocation, cost savings associated with buffers, and reduced material waste. By integrating BIM with construction operation simulations, we were able to create reliable construction plans that adapted to project changes. To validate our framework, we applied it to a structural steel model this was due to the significance of steel erections. It consists of the following steps: (1) preparing a BIM model to produce input data (2) composing a construction simulation at the operational level and (3) obtaining productivity dynamics from the BIM-integrated simulation. ![]() The resulting plan includes specific commands for retrieving the required information from BIM and executing operation simulations. To develop this framework, we examined critical factors affecting productivity at the operational level, and then forecast the productivity dynamics. To minimize variations, this research presents a Building Information Modeling (BIM)-integrated simulation framework for predicting productivity dynamics at the construction planning phase. Specifically, the distance between planning and execution brings cost overruns and duration extensions. Traditional construction planning, which depends on historical data and heuristic modification, prevents the integration of managerial details such as productivity dynamics.
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