Analyzing institutional co-authorship is essential for understanding collaborative https://4equality.info/understanding-5/ patterns and identifying core research forces in ML and AI applications for construction cost forecasting (Yevu et al., 2021). A country-level co-authorship network was constructed using VOSviewer to examine international collaboration patterns. Cheng’s high-impact work on evolutionary fuzzy neural networks, support vector machines, and hybrid intelligent models for construction cost estimation, including early ANN optimization models, established a key knowledge base. This cluster has been active since 2016, spanning the longest period, and its publications have received up to 456 citations, indicating foundational contributions to the field. The network exhibits a moderate level of centralization, with a few prolific authors acting as critical bridges. A minimum document threshold of 2 was applied, resulting in 65 core authors selected from 368 contributors.

Therefore, there is currently no single optimal model suitable for all engineering scenarios, as clear trade-offs remain among accuracy, transparency, and applicability. Although machine learning and artificial intelligence models generally outperform traditional statistical methods, significant differences still exist among models in terms of predictive accuracy, interpretability, data requirements, and engineering applicability. Previous studies have highlighted that combining scientometric analysis with qualitative synthesis allows researchers to identify macro-level trends while gaining deeper methodological and technical insights, ultimately forming a more systematic knowledge framework (Zang et al., 2025). Building on the preceding scientometric analysis, this section provides a systematic qualitative review of the literature to examine the characteristics, methodological development, and application trends of ML and AI in construction cost forecasting. Another important cluster consists of AI cross-application journals, including Expert Systems with Applications, Advanced Engineering Informatics, and Journal of Computing in Civil Engineering.

Overall, the field has evolved dynamically from early validation of classical algorithms toward recent multimodal integration and application optimization. The network exhibits a highly interconnected hub structure, with central keywords such as “machine learning,” “neural networks,” and “artificial intelligence,” forming multiple dense clusters. In ML- and AI-based construction cost forecasting, the keyword co-occurrence network reveals the core thematic structure, evolution pathways, and emerging research areas. This study employed VOSviewer to construct a keyword co-occurrence network (Zang et al., 2025), aiming to reveal the core thematic structure and research hotspots in the field. Despite being the primary contributor, its connections to other institutions are minimal, indicating high output but limited external collaboration. Node size represents publication activity, line thickness indicates collaboration strength, and the overall layout is relatively dispersed, showing multiple regional clusters and highlighting predominantly intra-institutional collaboration with limited cross-regional integration.

C. Assessing and Mitigating Risks

While the BIM technology is widely adopted, integrated, and used by AEC companies, one of the biggest problems that the construction industry continues to face is project delays, which inflate the costs of the projects https://newsplaces.net/the-main-tasks-of-the-wooden-bathhouse-what-to.html by as much as 20% or more . The adoption of ML on construction sites can take the level of safety to new heights. For example, if a firm wants to customize its office space based on its specific needs, ML can help predict the frequency of use for each room and present a design that is apt for the needs of the people. Currently, the applications of deep learning in this field are scarce compared to other digital technologies like machine learning (ML) and BIM. These systems can alert safety managers in real time when hazards are detected, enabling immediate intervention before accidents occur. Investing in comprehensive data collection and management infrastructure, including standardized data formats and integrated project management platforms, is a prerequisite for successful machine learning implementation.

Artificial Intelligence for the Built Environment

• Furthermore, Transformer- and Long Short-Term Memory (LSTM)-based temporal models are capable of capturing dynamic factors, including fluctuations in material prices and market conditions.
• In the construction industry, AI plays a crucial role in turning aspirations into realities by enhancing efficiency, safety, and productivity.
• Examples include GRU/LSTM-based highway cost index forecasting models and high-rise building cost estimation systems integrated with BIM attributes.
• A minimum document threshold of 2 was applied, resulting in 65 core authors selected from 368 contributors.
• The network exhibits a moderate level of centralization, with a few prolific authors acting as critical bridges.
• These validation findings show that the study’s model matches the survey experts’ views.

These metrics are widely applied due to their simplicity and clear physical interpretation. Consequently, construction cost is not a static outcome determined by a single variable, but a dynamic product of interactions among intrinsic project attributes, resource constraints, and external environmental factors. Qualitative analysis indicates that influencing factors in construction cost prediction are complex, interrelated, and dynamic.