The integration and impact of artificial intelligence (AI) and cloud computing (CC) technology in the construction industry (CI) would support their implementation process and adoption. However, there is a lack of research in the extant literature, and recent advances in this field have not been explored. As such, the key research question focuses on the extent of existing literature, main research hotspots, and recent advances (i.e., research gaps and future directions) in AI in CC in the CI. To address this research question, this study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions. This review study used a four-step bibliometric-systematic review approach consisting of literature search, literature screening, science mapping analysis, and qualitative dis-cussion. The results found four main research hotspots, namely (1) construction project performance indicators, (2) data analysis and visualization, (3) construction quality control and safety, and (4) construction energy efficiency. These findings would provide valuable insights for scholars and practitioners seeking to understand and integrate AI and CC technology applications in the CI. This review study will lay a better foundation for future developments in construction project management processes, data-sharing protocols, real-time safety monitoring, and ethical implications of AI and CC technologies.

3D Concrete Printing (3DCP) offers significant advantages over traditional construction such as faster construction time, reduced material wastage, and enhanced ability to execute complex architectural designs. The incorporation of various fibres and industrial wastes into 3DCP can improve performance and sustainability but introduces non-linear effects on compressive strength (CS) that are difficult to predict with standard laboratory methods. This study aims to develop reliable prediction models for the CS of 3DCP by employing advanced neural network and deep learning algorithms such as Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Radial Basis Functional Neural Network (RBFNN). A comprehensive database of 200 experimental instances of CS of 3DCP was collected from published literature. The database includes mixture constituents of 3DCP as inputs and CS as the output. The trained algorithms were validated by means of k-fold validation, error metrics, and residual assessment. Among the tested algorithms, the CNN model exhibited the highest predictive performance with a testing R² value of 0.95, demonstrating its robustness in modelling the complex behaviour of 3DCP. To enhance interpretability, Shapley (SHAP) and Individual Conditional Expectation (ICE) analyses were performed, identifying the water-to-cement ratio, loading direction, and fibre content as key factors influencing compressive strength. Finally, a graphical user interface (GUI) has been developed for stakeholders to implement the findings of this study practically.

Three-dimensional (3D) concrete printing (3DCP) is an emerging digital construction technology that enables geometrically complex structures with reduced labour, material waste, and formwork. However, the sustainability of 3DCP remains constrained by its heavy reliance on Portland cement, a major source of global CO₂ emissions. This study systematically examines metakaolin (MK) and biochar (BC) as sustainable additives for 3DCP, focusing on their independent effects on mechanical performance, printability, dimensional stability, and environmental impact. A comprehensive literature review (2015 to June 2025) identified 254 publications, of which 21 met the inclusion criteria for quantitative meta-analysis, contributing a total of 95 datasets for compressive and flexural strength. Pooled effect sizes were calculated using a random-effects model, supported by risk-of-bias and heterogeneity analyses. The results indicate statistically significant improvements in mechanical properties, with an overall pooled ratio of means (ROM) of 1.12 (95% CI: 1.06–1.20; I² = 48.9%), representing the overall mechanical performance effect across all datasets, while ROM for compressive and flexural strength was calculated separately in the main analysis. Meta-regression revealed that BC increased compressive and flexural strengths by 7% and 9%, respectively, while MK achieved greater enhancements of 21% and 13.4%. Optimum performance was observed at 15–20% MK for compressive strength and 10–15% for flexural strength, whereas BC performed best at 3–5% and 2–5%, respectively. BC contributed to CO₂ reductions of up to 43% through clinker substitution and biogenic carbon sequestration. These findings demonstrate that MK and BC are complementary eco-efficient modifiers capable of enhancing both structural and environmental performance in 3DCP. Future research should address long-term durability, standardisation of printing parameters, and cradle-to-grave life cycle assessments to strengthen practical implementation.

3D printed concrete (3DPC) is an advanced additive manufacturing technique which aids in creating complex structures with precision and efficiency. This innovative technology offers substantial benefits, including minimised material waste, enhanced project completion speed, and the capability to fabricate complex and distinctive designs. While significant progress has been made, existing review studies primarily address specific domains such as materials, technical and non-technical challenges, rheological parameters, and modelling. However, limited research has explored quantitative data on leading countries and institutions, collaboration networks, profiles of influential authors, and key journals. Furthermore, there is a lack of comprehensive qualitative insights into the managerial, sustainability and environmental, and economic aspects of 3DPC, as well as advancements in material compatibility, mix design methods, and applications of emerging technologies. This study addresses these gaps by conducting a bibliometric and systematic review of 3DPC research from 2015 to 2024, with data sourced from Web of Science and Scopus. The bibliometric analysis revealed a steady growth in 3DPC studies, with annual publications increasing by over 800% between 2018 and 2024, and major contributions from China (28%), Australia (10%), and the USA (7%). Tongji University emerged as the leading institution, accounting for 8% of the total publications. The systematic review discusses the key advancements in technology and structural development while uniquely focusing on the managerial aspects of 3DPC. By integrating bibliometric and qualitative insights, this review provides a comprehensive understanding of the current state and future potential of 3DPC, offering valuable guidance for researchers and practitioners aiming to advance this transformative technology.