Concrete structures deteriorate over time due to environmental exposure and mechanical stress, leading to various types of damage such as cracking, spalling, corrosion, and exposed rebar. Automated detection using deep learning-based computer vision techniques is limited by the lack of high-quality, annotated data sets. To address this challenge, this paper presents multidamage monitoring of concrete structures (MDMCS), a data set of 1,200 images with precise pixelwise annotations involving four types of damage (cracking, spalling, corrosion, and exposed rebar) and diverse lighting conditions and material textures. The data set was evaluated using six state-of-the-art segmentation models, validating the efficacy of the data set and providing benchmarks for damage detection models. MDMCS will facilitate advances in artificial intelligence-powered structural monitoring and robot-assisted automatic inspection for improving the operation and maintenance of concrete structures.

Accurate assessment of the flow behaviour of fresh concrete is essential for ensuring stability, mechanical performance, and durability. Flow behaviour governs key construction processes including mixing, pumping, and placement. While rheometers provide precise measurements, they are costly, complex, and impractical for field use. Consequently, alternative techniques have gained attention for real-time and on-site monitoring. These approaches evaluate flow characteristics and track mixing by capturing measurable signals such as images, sound, vibration, or power consumption. Compared to rheometers, they offer advantages in speed, automation, and minimal disruption. This review critically examines the principles, accuracy, technology readiness, applicability, and limitations of these emerging approaches, and explores future directions. It also emphasizes artificial intelligence (AI) integration to enhance data processing and automation and examines multimodal strategies such as multi-sensor fusion, multi-source data fusion, and large language models to improve analysis and decision making. These advances support the vision of Industry 4.0 through intelligent and connected concrete production systems.

This paper proposes an interpretable machine learning approach for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design. A database consisting of 296 samples was constructed, and 14 input variables related to asphalt properties, aggregate gradation, and fiber characteristics were selected for modeling. Eight machine learning models, namely TabPFN, ANN, SVR, RF, XGBoost, LightGBM, FLAML, and FT-Transformer, were developed and compared. The results show that all eight models achieved satisfactory predictive capability, whereas TabPFN overall achieved the best performance in the Monte Carlo cross-validation, with the lowest average RMSE of 0.34 ± 0.10, the lowest average MAE of 0.21 ± 0.02, a relatively low average MAD of 0.14 ± 0.03, the highest average R² of 0.85 ± 0.08, and the highest composite score of 0.81. SHAP analysis further indicated that splitting strength prediction was mainly governed by a limited number of dominant variables, among which Ag9.5, AC, Du, FT, and Ag4.75 were the most effective parameters. In addition, favorable parameter ranges for improving splitting strength were quantified, such as Ag9.5 < 66.8%, AC < 5.4 wt.%, Du > 134.7 cm and Ag4.75 < 45.0%. Finally, a graphic user interface platform integrating prediction and SHapley Additive exPlanations-based interpretation was developed to improve the accessibility and practical applicability of the proposed framework.

Conventional asphalt concrete has a limited lifespan due to cracking, deformation, and environmental degradation, driving the development of fiber-reinforced asphalt concrete (FRAC). However, key gaps remain in current data-driven FRAC studies due to small and homogeneous datasets, “black-box” machine learning models, and trade-offs between mechanical-sustainable performance, failing to provide a transparent understanding of features governing FRAC behaviors. This paper proposes a framework integrating explainable artificial intelligence and life cycle assessment (LCA) to advance mechanical and sustainable design of FRAC. A dataset of 2490 laboratory samples covers 15 input features and 3 mechanical outputs. Eight machine learning models, along with a voting ensemble strategy, were optimized using Genetic algorithm for hyperparameter tuning. The optimized voting ensemble achieved an average prediction performance of R² = 0.87, RMSE = 1.09, MAPE = 11.96%, and MAE = 0.60 across the three mechanical targets, indicating robust and reliable predictive capability. SHapley Additive exPlanations (SHAP) analysis and linear non-gaussian acyclic causal inference quantified global/local feature impacts and pairwise interactions. LCA evaluated economic and environmental impacts and derived strength-normalized sustainability metrics. Finally, an interactive graphic user interface platform was developed for predictions, SHAP interpretations, and LCA outcomes. This data-driven approach establishes a paradigm for intelligent FRAC design, harmonizing mechanical performance with sustainability.

As urbanization accelerates, aging infrastructure demands more advanced inspection methods for structural health monitoring. The growing integration of artificial intelligence (AI) and computer vision technologies has significantly enhanced damage detection accuracy while simultaneously reducing inspection time and operational costs. Despite these advantages, the adoption of AI-based technologies in infrastructure maintenance remains limited due to challenges related to data. One major issue is the lack of comprehensive, task-specific annotated datasets. Another is the poor quality of images captured by drones or mobile devices, which are often affected by noise, blurring, and inconsistent lighting. Although recent advances in generative AI offer promising support for structural health monitoring, it remains unclear which models are best suited for specific tasks. This study examines the use of generative AI in structural health monitoring, focusing on key challenges such as limited datasets and low-quality image restoration. The review covers a range of generative AI technologies, outlining their principles, strengths, limitations, and representative applications to support the selection of appropriate tools for specific tasks. Generative AI models enable accurate image segmentation and structural anomaly detection using limited training data. The paper also explores new opportunities for integrating multi-modal generative AI to enhance human–computer interaction in support of structural health monitoring. A framework is proposed to streamline the use of generative AI technologies for data augmentation, image restoration, damage inspection, and human–computer interaction in structural health monitoring.

The study explores dataset preparation, machine learning (ML) model training, interpretation, and life cycle assessment (LCA) to predict and enhance the performance and sustainability of rubberized concrete. A large dataset comprising 1209 collected samples with nine input features was used to train and evaluate six machine learning models. Among the six models, the Light gradient boosting machine (LightGBM) model achieved the highest prediction accuracy on the testing dataset, with an R² value exceeding 0.96, a MAPE of 8.31 %, a MAE of 2.36 MPa, and a RMSE of 3.25 MPa. The SHAP algorithm was used to interpret predictions and identify key factors influencing compressive strength. Rubber content and water-to-cement ratio reduced strength, while longer curing time, more superplasticizer, higher fine aggregate content, and a greater silica fume-to-cement ratio improved it. Coarse aggregate, crumb rubber size, and cement content had minimal impact. Optimal performance was achieved with: Rubber content <55.92 kg/m³, w/c ratio <0.4, curing time >26 days, superplasticizer >3.7 kg/m³, fine aggregate >642 kg/m³, and silica fume/cement ratio >2.5 %. LCA results show that, although rubberized concrete offers no clear advantage over conventional concrete in cost, carbon, or energy, the lower strength and higher superplasticizer use of rubberized concrete lead to greater strength-normalized impacts, and therefore its value lies more in waste recycling and toughness than in strength-based sustainability. The unique advantage of this research lies in the development of a ML-LCA integration framework that synthetically balances performance prediction and sustainability assessment of rubberized concrete, with delivering actionable mix design recommendations, identifying key and low-impact variables, and revealing trade-offs among strength, cost, and environmental performance.

Geopolymer concrete (GPC) is a sustainable alternative to Portland cement concrete by eliminating Portland cement. However, using alkaline activators compromises the sustainability of GPC. This paper presents a comprehensive assessment of the cradle-to-gate life cycle cost, carbon footprint, and energy consumption of 2304 GPC mixtures which represent the state-of-the-art dataset, aiming to establish a holistic understanding of the impacts of GPC design parameters on mechanical and sustainability performance. The cost-benefit characteristics of solid wastes are considered, and strength-normalized sustainability parameters are discussed. Results reveal that the formulation of GPC plays critical roles in mechanical and sustainability metrics. Inappropriate use of alkaline activators and solid wastes can largely compromise both mechanical strength and sustainability metrics, lower than their Portland cement counterparts. Based on the large dataset, this paper identifies the appropriate upper and lower bounds for various ingredients to guide the design of GPC for balanced mechanical strength and sustainability metrics.