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 https://real-apartment.com/production-of-quality-slings-here-you-will-find.html 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.
While the BIM technology is widely adopted, integrated, and used by AEC companies, one of the biggest problems that the construction industry continues https://heplerbroom.com/insights/events/tiffany-and-whitlock-glave-to-speak-on-legal-ramifications-of-artificial-intelligence-in-construction/ to face is project delays, which inflate the costs of the projects 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.
Analyzing institutional co-authorship is essential for understanding collaborative 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.
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.
3 Challenges in handling data and algorithm integration
Chapter 6 concludes the https://businesselevatepro.com/tag/industry study, summarizing key findings and providing corresponding research insights. Using VOSviewer, keyword co-occurrence, research hotspots, and academic collaboration networks were visualized to reveal the knowledge structure and evolution of the field (Al Husaeni, 2023). Accordingly, this study aims to provide a comprehensive review of AI and ML applications in construction cost prediction. The data supporting the findings of this study are available on reasonable request from the corresponding author.
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.
- This proactive approach enables teams to hold safety briefings and address potential risks before they escalate, ultimately improving overall site safety.
- The adoption of ML on construction sites can take the level of safety to new heights.
- At the forefront of this technological revolution is artificial intelligence (AI), which is transforming the way businesses operate.
- Journal co-citation analysis was conducted to reveal the underlying structure of knowledge in ML and AI applications for construction cost forecasting, helping to identify core sources and track the evolution of research themes (Xu et al., 2022).
- Data augmentation techniques address small sample sizes, while methods like SMOTE handle class imbalance in classification tasks.
- Deep learning is a subfield of machine learning based mostly on neural networks.
- SVM shows strong generalization ability under small-sample conditions, but its training efficiency becomes limited when applied to large-scale datasets.
- ML and AI applications in construction cost prediction have progressed from theoretical validation to practical implementation, forming a technical ecosystem that spans the full project lifecycle and adapts to diverse scenarios.
- As shown in Figure 2, only a few publications appeared between 1994 and 2008, indicating that ML and AI applications in construction cost forecasting were still in the exploratory and theoretical research phase during this period.
- When ML software is used in a construction project, it increases the level of productivity.
- The construction industry, one of the most under-digitized sectors, is currently undergoing a significant transformation due to the emergence of artificial intelligence (AI).
The capability of enhancement in BIM through ML is huge, though the implementation of these kinds of advanced technologies may not be devoid of challenges. ML and BIM integration has recently gained considerable attention from the construction industry to solve some of these persistent pains, such as delay, cost overrun, and inefficiency issues. This research proposes a new quantitative framework that incorporates ML algorithms with BIM technology in predicting construction delays. This study is likely to give useful information on practical usage of leading technologies in construction and thus enable better decision-making, enhancing the overall efficiency of projects. Addressing how ML and BIM are integrated may achieve major leaps in construction management by making more real and timely predictions of project delays.
- 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.
- Much strength of BIM lies in the way it allows collaborative project management, integrated datasets in every way possible along the integral life cycle of the construction project .
- Given the possible results of this research, standard operational procedures can be redefined by including data-driven predictions at the levels of planning and execution of construction projects.
- Furthermore, data are often concentrated within a single country or region, lacking cross-regional validation.
Assessing the impact of claims on construction project performance using machine learning techniques
For static cross-sectional data, K-fold cross-validation (K-fold CV) randomly partitions datasets into training and validation subsets and iteratively evaluates model performance, mitigating overfitting from a single data split. Early-stage design decisions act as front-end control variables, establishing basic cost boundaries and producing a “decision upfront, realization later” dynamic. Feature engineering is central to enhancing model performance, including correlation analysis, PCA, and other dimensionality reduction techniques to select key variables, mitigate multicollinearity, and reduce noise. These journals focus on algorithm and intelligent system applications and were most active in the early stages, reflecting the integration of expert systems, neural networks, and other AI techniques into civil engineering research. The study also focused on a limited set of variables and machine learning techniques, which may not have covered all project delay reasons and methods.
VOSviewer was employed to construct an author co-authorship network to reveal collaboration patterns among core researchers in ML and AI applications for construction cost forecasting. The attention of the construction management field toward intelligent cost estimation steadily increased after 2009. As shown in Figure 2, only a few publications appeared between 1994 and 2008, indicating that ML and AI applications in construction cost forecasting were still in the exploratory and theoretical research phase during this period. By analyzing the annual publication counts of the 138 core articles included in this review, the development trajectory and growth trends of ML and AI applications in construction cost forecasting can be clearly illustrated.
Understanding Machine Learning and Its Relevance to Construction
Furthermore, data are often concentrated within a single country or region, lacking cross-regional validation. Despite significant advances in applying ML and AI to construction cost prediction, practical implementation still faces multi-dimensional limitations. Examples include GRU/LSTM-based highway cost index forecasting models and high-rise building cost estimation systems integrated with BIM attributes. By integrating core parameters such as building area and structural type, these tools can deliver minute-level cost estimates with prediction errors typically controlled within 10%–15%, significantly improving decision efficiency.
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