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Automating Bridge Construction Scheduling Data with BIM and Machine Learning
No full textConventional construction scheduling techniques often fall short due to inefficiencies, the prevalence of human error, and the lack of reliable scheduling data. Despite efforts to obtain better scheduling data and automate the process using Building Information Modelling (BIM) as a primary source of information, challenges persist. BIM models contain inconsistencies that can compromise their scheduling effectiveness. Moreover, existing automation efforts face obstacles, including complexity and scalability issues. These efforts frequently overlook the intricacies of accurately modelling construction tasks, neglecting resource considerations, subprocesses, and structured information management, which further complicates the creation of reliable and efficient construction schedule data. This research proposes a framework designed to systematically obtain scheduling data in an automated manner, aligning with a Work Breakdown Structure (WBS) to ensure organisational clarity. By incorporating subprocesses and focusing on resource constraint calculations and balancing. Furthermore, the framework structures scheduling data into a CSV format, facilitating easier analysis and integration with project management tools. The research follows the Design Science Research methodology. This multi-stage framework integrates BIM data extraction, automated element labelling, creation of a custom WBS, construction sequence, and resource balancing. The framework was developed using Python coding and libraries, facilitating seamless transitions between stages without manual intervention. The framework's performance is evaluated using 4D BIM software, assessing the generated data and logic in a girder bridge projects. This innovative framework enhances the accuracy and efficiency of construction data scheduling through automation. It reduces manual intervention, organises data effectively, and improves project timeline reliability. The use of 4D BIM further illustrates the practical application of this data in bridge projects, showcasing a scalable and robust contribution to construction automation
Investigation of Energy Efficiency in The Housing Sector Within The Framework of Sustainable Building Design
No full textToday, increasing environmental concerns and worries about energy security have made the efficient use of energy in buildings of critical importance. Buildings account for a significant portion of global energy consumption and contribute to greenhouse gas emissions. In this context, it is of great importance to evaluate the energy performance of buildings and make the necessary improvements to reduce their energy consumption. In the construction sector, most of the energy consumption is realised in residential buildings. Therefore, by focusing on energy efficient works primarily on residential buildings, a significant reduction in energy consumption can be achieved and negative impacts on the environment can be minimised. Energy efficient building design studies are generally carried out on the basis of a building, floor or neighbourhood through thermal insulation of the building envelope. This study distinguishes itself from other studies by taking into account the characteristics of Island-based settlements in the implementation of energy-efficient building design. In addition, it is an important feature of this study that energy analyses in buildings are carried out in a comprehensive manner to include the use of electricity as well as natural gas. In this study, an island-based urban regeneration project located in the Mediterranean climate zone is considered and research on energy efficiency is carried out on selected base buildings. The analyses were performed using the Design Builder simulation program of the EnergyPlus dynamic thermal simulation engine and the Solar PV simulation program with PVsyst software. An increase in initial investment cost, the rate of energy savings, and the values of energy consumption were calculated on an annual basis
