Traditional construction planning, which depends on historical data and heuristic modification, prevents the integration of managerial details such as productivity dynamics. Specifically, the distance between planning and execution brings cost overruns and duration extensions. To minimize variations, this research presents a Building Information Modeling (BIM)-integrated simulation framework for predicting productivity dynamics at the construction planning phase. To develop this framework, we examined critical factors affecting productivity at the operational level, and then forecast the productivity dynamics. The resulting plan includes specific commands for retrieving the required information from BIM and executing operation simulations. 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. To validate our framework, we applied it to a structural steel model; this was due to the significance of steel erections. By integrating BIM with construction operation simulations, we were able to create reliable construction plans that adapted to project changes. 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.

This paper presents a framework for integrating building information modelling (BIM) and object-oriented physical modelling-based building energy modelling (BEM) focusing on thermal simulation to support decision-making in the design process. The framework is made of a system interface between BIM and Modelica-based BEM and the visualization of simulation results for building designers. The interface consists of the following two major features: (1) pre-processing BIM models to add required thermal parameters into BIM and generate the building topology and (2) translating BIM to Modelica-based building energy modelling automatically and running the thermal simulation. The visualization component presents the simulation results in BIM for designers to understand the relationship between design decisions and the building performance. For the framework implementation, we have created a ModelicaBIM library and utilized the Modelica Buildings library developed by the Lawrence Berkeley National Laboratory. We conducted a case study to demonstrate and validate the framework simulation results.

This paper presents a new approach to translate between Building Information Modeling (BIM) and Building Energy Modeling (BEM) that uses Modelica, an object-oriented declarative, equation-based simulation environment. The approach (BIM2BEM) has been developed using a data modeling method to enable seamless model translations of building geometry, materials, and topology. Using data modeling, we created a Model View Definition (MVD) consisting of a process model and a class diagram. The process model demonstrates object-mapping between BIM and Modelica-based BEM (ModelicaBEM) and facilitates the definition of required information during model translations. The class diagram represents the information and object relationships to produce a class package intermediate between the BIM and BEM. The implementation of the intermediate class package enables system interface (Revit2Modelica) development for automatic BIM data translation intoModelicaBEM. In order to demonstrate and validate our approach, simulation result comparisons have been conducted via three test cases using (1) the BIM-based Modelica models generated fromRevit2Modelicaand (2) BEM models manually created using LBNL Modelica Buildings library. Our implementation shows thatBIM2BEM(1) enables BIM models to be translated intoModelicaBEMmodels, (2) enables system interface development based on the MVD for thermal simulation, and (3) facilitates the reuse of original BIM data into building energy simulation without an import/export process.

The development of separate building performance simulation tools has brought about a significant need for the integration of multi-domain simulations that would enable multiple building performance analyses to be conducted from a single building model. Insufficient data integration between the tools and Building Information Modeling (BIM) currently prevents the efficient production of comprehensive building performance analyses. To overcome this problem, a multi-domain simulation tool, effective and efficient data, and a process integration methodology are all required. Object-Oriented Physical Modeling (OOPM) has emerged as a form of object-oriented modeling capable of supporting multiple domain simulations. Adoption of OOPM into building performance simulations with BIM is particularly interesting as a means of facilitating model translations between BIM and BEM (Building Energy Modeling). Specifically, complex data translation from the building topology in BIM to the space boundary conditions in BEM is both labor intensive and time consuming. This research uses case studies to investigate the feasibility of automatically translating a building topology from BIM to OOPM-based BEM. We included numerous preconditions in order to incorporate various object semantic differences into each model translation. Our research indicates that this adoption approach allows seamless model translations from BIM to OOPM-based BEM, which supports efficient and effective thermal simulations and facilitates the reuse of BIM data in multi-domain simulations.

Hourly global solar radiation in a weather file is one of the significant parameters for improving building energy performance analyses using simulation programs. However, most weather stations worldwide are not equipped with solar radiation sensors because they tend to be difficult to manage. In South Korea, only twenty-two out of ninety-two weather stations are equipped with sensors, and there are large areas not equipped with any sensors. Thus, solar radiation must often be calculated by reliable solar models. Hence, it is important to find a reliable model that can be applied in the wide variety of weather conditions seen in South Korea. In this study, solar radiation in the southeastern part of South Korea was calculated using three solar models: cloud-cover radiation model (CRM), Zhang and Huang model (ZHM), and meteorological radiation model (MRM). These values were then compared to measured solar radiation data. After that, the calculated solar radiation data from the three solar models were used in a building energy simulation for an office building with various window characteristics conditions, in order to identify how solar radiation differences affect building energy performance. It was found that a seasonal solar model for the area should be developed to improve building energy performance analysis.