Technological advancements and methods for tracking CO₂ within the construction industry have enhanced emissions monitoring. However, traditional CO₂ data collection methods often rely on manual processes that lack real-time capabilities, limiting their effectiveness in offering meaningful guidance for reducing emissions. This study employs a literature review and expert opinions to examine current practices and advancements in CO₂ tracking for the construction sector, assessing their effectiveness and potential for widespread industry adoption based on European Union regulations. Ultimately, this work contributes to the advancement of CO₂ monitoring by providing recommendations that meet regulatory requirements and stakeholder demands for sustainable construction practices.
The digitalization of the construction industry enhances efficiency through advanced technologies but increases cyber vulnerabilities. Existing research lacks comprehensive frameworks for risk identification and automation, leaving gaps in addressing cybersecurity challenges. Advances in language models offer potential, but limitations like outdated datasets and small architectures hinder their effectiveness. This study addresses these issues by collecting an up-to-date dataset to fine-tune the GPT-4o Mini model, renowned for its size and reasoning capabilities. The fine-tuned model outperforms others in identifying phase-specific cyber risks, generating a more thorough risk checklist. Its scalability suggests potential applications in broader risk management tasks, enabling industry-wide adoption.
A framework for monitoring HVAC data streaming and detecting anomalies is presented. Latent Dirichlet Allocation identifies three states corresponding to heating, cooling, and baseline system behaviors, while the Dirichlet distribution detects proportion anomalies independent of total data volume. Using 34 months of sensor data from an academic-residential building, the framework reveals seasonal and operational trends, with notable anomalies linked to maintenance events. This method enables facility managers to monitor system states and diagnose deviations efficiently but requires sufficient historical data and expert state interpretation. This low-complexity approach provides a practical tool for real-time HVAC monitoring in dynamic environments.
This study addresses the need for precise maintenance cost prediction by developing unit cost models based on element-level repair data. Using infrastructure maintenance regulations in Korea, a systematic facility breakdown structure was established, and a cost database was compiled from maintenance records. The Extreme Gradient Boosting (XGBoost) algorithm was applied to develop cost prediction models for different repair methods in structural elements such as decks, piers, and drainage systems. The results showed high predictive accuracy, with bridge models averaging 87.2% and tunnel models 85.5%. The findings provide data-driven insights to enhance maintenance planning and support cost-efficient infrastructure management strategies.
In Italy defining common and standardized procedures to enable a structured Digital Building Permit adoption are consistent issues. However, the Decree to promote digitization of public tenders (Decree n. 50, 2016) had been confirmed and renovated in 2023 (Decree n. 36, 2023). At the national level barriers and burdens are completely blocking construction activities and urban renovation due to uncertainty in approval procedures and legislative doubts. These deficiencies are not solved by the public bodies, and practitioners and companies are struggling to operate.
Foundation Models like Large Language Models (LLM) and Large Vision Models (LVM) show huge potential in automating processes in building design, construction, and operation. However, there are concerns about their capability to create coherent, usable, sustainable, and safe architecture. This paper analyzes the challenges and potentials of LLM and LVM in the AEC sector. We discuss a collection of relevant AEC modalities like CAD models, semantic graphs, and time series and how they could integrate into the current landscape of models. From the analysis we derive research directions toward the development of domain specific “Building Foundation Models”.
Fragmentation in construction supply chains constrains performance improvements. Building Information Modelling (BIM) is increasingly recognised as a digital enabler of supply chain integration (SCI), yet its specific integration capabilities remain underexplored. This study conducts a systematic literature review to identify BIM integration capabilities, BIM-enabled integration practices (BEIPs), and supporting technical mechanisms. Findings reveal BIM capabilities to facilitate information, process, relational, and technology integration. BIM technical mechanisms (BTMs) underpin, while complementary technical mechanisms (CTMs) enhance, these capabilities. The study advances understanding of BIM’s role in SCI and informs practitioners on technical underpinnings critical to achieving digital integration in construction projects.
