As building information modeling (BIM) systems continue to be widely adopted, there is an increasing demand for an active construction schedule management system with more advanced decision-making capabilities. For example, if overlapping between construction activities is significant, the performance of construction operations for the corresponding activities may deteriorate. Thus, a viable construction schedule should be formulated in order to minimize overlapping of proximate construction activities. An active system can be regarded as a certain solution for this issue. The purpose of this study is to develop a systematic methodology and computer system for an optimal construction schedule simulation that minimize overlapping activities for the enhancement of a project’s operational performance. This study centers on identifying overlapping activities, applying fuzzy theory, and analyzing risk levels for schedule overlap issues. In addition, genetic algorithm (GA) theory is adopted for the mini...

Evaluating the site-selection process for photovoltaic (PV) plants is essential for securing available areas for solar power plant installation in limited spaces. Although the vicinities of highway networks can be suitable for installing PV plants, in terms of economic feasibility, they have rarely been investigated because the impacts of various factors, including geographic or weather patterns, have not been analyzed. In this respect, this study conducts a case study on selecting the site for PV-panel installation in the vicinity of a highway (e.g., slopes) by integrating geographic information system (GIS) and building information model (BIM) techniques. Using location (e.g., highways, lakes, rivers), monthly solar power output, and orographic (e.g., slope) data, suitable regions are identified with the geo-spatial analysis; then, the amount of power that can be generated is evaluated in the regions. For the proposed sites, the surface conditions and potential PV systems are transformed and visualized in a BIM environment. In the results, the power output at optimal sites selected from the case area was computed at a total of 8227 MWh and was transformed into solar-panel families in three-dimensional environments. Thus, this study may help to identify optimal sites for PV plants in the unused areas near highways.

This study proposes a 3D intelligent object model in highway construction to electronically represent a large amount of information in highway projects based on the international standard ISO 10303. Also known as STEP (Standard for the Exchange of Product), ISO 10303 is an open standard, and has been used for data exchange in building projects of the AEC industries. Motivated by the success from the building industry, this research attempts to apply an object based intelligent model in 3D highway modeling. In order to test the feasibility of the proposed approach, a prototype was developed and tested on a highway project in Seoul, Korea with a team led by Korean Institute of Construction Technology (KICT). The proposed 3D intelligent model was applied to build a 3D model and automatically derive cost/schedule estimates from the 3D object model. To develop a 3D object model in highway road construction, this paper explores different options such as Industry Foundation Classes (IFCs) and LandXML and establish a comprehensive object model for highway structures with different components such as roads, a bridge, and a tunnel as well as topographical representations that could be used for cost/schedule estimation.

In the AECO field, there have been many efforts to transform the design, construction, and maintenance methods that have been carried out in the traditional way based on document-based data into digital ones. BIM makes it possible for the information necessary for the entire construction process to be transformed into a language that computers can understand. In the field of architecture, BIM has been actively used throughout the entire construction life cycle through steady research since the 2000s, while COBie was developed in order to deliver information generated in the design and construction phase to the maintenance phase based on the BIM environment. In the field of infrastructure, however, digital data-based maintenance information management has not been actively studied. In particular, in the case of a port, once a facility is built, it has to be maintained for a longer period than other facilities, so facility maintenance is of utmost importance. Therefore, this study applied COBie, which is being used in the architecture field (building), to the port field. To this end, the COBie standard format in the port field was developed based on the port facility object breakdown structure and property breakdown structure. The port COBie developed through this study is included when the BIM model is converted to the IFC model so that facility management can be performed through the IFC-based viewer in the future. The port COBie schema developed in this study can be expanded to other fields such as roads and bridges in the future.

Accurate prediction of building thermal demands is essential for evaluating energy efficiency and optimizing performance within energy management systems. This need has become increasingly critical due to growing environmental concerns surrounding energy consumption and its ecological impact. Reliable estimation of energy demands supports informed decision-making in resource management and carbon footprint reduction, promoting sustainable and technologically advanced construction practices. This study investigates the predictive accuracy of several ensemble boosting algorithms in estimating thermal energy demands (heating and cooling loads) using different architectural parameters. The performance evaluation using RMSE metrics demonstrated XGBoost’s dominance across both thermal prediction tasks. For heating load estimation, this algorithm yielded the lowest error rate of 0.309 kW, surpassing CatBoost’s 0.318 kW and LightGBM’s 0.351 kW results. In cooling load forecasting, XGBoost maintained its leading position with 0.731 kW RMSE, outperforming LightGBM (0.907 kW) and CatBoost (0.917 kW), which occupied the subsequent ranking positions. Testing phase findings across heating and cooling load predictions confirmed that the XGBoost ensemble methodology successfully improved estimation accuracy when applied to thermal demand modeling with associated parameters. Moreover, SHAP analysis revealed that glazing area distribution and orientation had minimal impact on heating load predictions, while overall height and relative compactness were the most influential. Similarly, orientation and glazing area distribution parameters contribute the least to the prediction of cooling load, whereas overall height and relative compactness parameters have the most significant influence. These findings confirm the effectiveness of XGBoost in enhancing prediction accuracy for residential thermal demand modeling. Graphical abstract