This study evaluated the effects of simultaneous internal-external carbonation curing on the mechanical performance, CO2 uptake, and microstructural evolution of belite-rich cement containing acid-treated rice husk biochar. Compared with external carbonation alone, simultaneous internal-external carbonation accelerated the early carbonation reaction, as evidenced by greater carbonation degree and increased CO2 uptake. Biochar addition improved early-age performance under carbonating conditions, most likely by providing a porous internal framework that promoted moisture redistribution and carbonate precipitation. At 28 days, however, the simultaneously carbonated mixtures exhibited lower compressive strength than the corresponding externally carbonated mixtures. Physicochemical analysis results indicated that prolonged carbonation progressed beyond portlandite consumption and was accompanied by spectral and thermal changes consistent with partial decalcification of C-S-H and silicate polymerization. These findings indicate that simultaneous internal-external carbonation is effective for improving early-age carbonation curing efficiency, but that carbonation intensity and duration should be optimized to minimize adverse effects on long-term performance.
This paper presents a comparative life-cycle assessment of two integrated energy and seismic retrofit solutions for conventional reinforced concrete buildings with masonry infills. The assessment builds on full-scale experimental retrofits conducted at the European Laboratory for Structural Assessment, which confirmed the strong seismic performance of both systems and supplied detailed material and construction data. The two approaches combine seismic strengthening with thermal insulation: a bio-based cross-laminated timber system with mineral wool, and a mineral-based textile-reinforced mortar system with extruded polystyrene. Each retrofit is evaluated separately to support selection of the most suitable combined energy–seismic strategy. A cradle-to-grave life-cycle assessment is performed using the Environmental Footprint method across 16 impact categories, over a 50-year reference period, with results expressed per square metre of usable floor area. Although both systems show similar global warming potential, other impact categories reveal important trade-offs, offering guidance for sustainable retrofit decisions in ageing European buildings.
Optimizing the pore structure during early cement hydration is crucial for enhancing the mechanical properties and long-term durability of cement-based materials. In this study, microbially induced carbonate precipitation (MICP) is introduced into cement slurry, and the influence of cementation solution concentration (CSC) on the macro/micro performance of microbial cement slurries is investigated. Results reveal that microbial mineralization competes with cement hydration for Ca2+, thereby inhibiting the hydration process. Excessively high CSC suppresses microbial activity, whereas insufficient concentration limits Ca2+ availability, thereby reducing MICP efficiency and inhibiting cement hydration. At 1 mol/L CSC, MICP exhibits the most pronounced effect on refining the pore structure. Compared with pure cement slurry, the compressive strength increases by 15.43% and permeability decreases by 46.48% at 28 d. MICP primarily reconstructs the pore structure of cement slurry through the physical pore filling of CaCO3 precipitation and the loosening of the C-S-H gel structure by "kinetic trapping".
This paper investigates self-healing functionalisation strategies for glass textile-reinforced CEM II-based mortars (TRM), via matrix and textile, using crystalline admixtures (CA) and/or silica fume (SF). Pre-cracked specimens were monitored over six months, with self-healing assessed through capillary absorption, microstructural analyses and mechanical tests, after freshwater and saltwater exposure, and repeated versus uninterrupted crack-healing cycles. TRM incorporating CA and 10% SF achieved the best performance, with sorptivity healing indices up to 82% (nine times higher than the reference), crack closure up to 50% and full cracked stiffness recovery after six months of freshwater immersion. Saltwater accelerated superficial crack sealing (60-87%) without proportional recovery of durability or stiffness. Mortar functionalisation enabled depth-through healing, with CA and SF acting as latent healing reservoirs; XRD confirmed portlandite consumption alongside calcite precipitation. Textile functionalisation acted as a local, interface-driven healing mechanism. A global healing index accounting for crack geometry is proposed, supporting comparative design-oriented assessment.
This study advances lightweight aggregate concrete for 3D-printing by addressing continuous fabrication and establishing links between composition, manufacturing process, and composite performance. Using virgin cork granules (0.24 v/v), supplementary cementitious binders (e.g., blast-furnace slag and silica fume), and an automated material delivery system, the research examines the resulting printed material’s microstructure, mechanical, and thermal properties. The results showed that fabrication of lightweight, printable concrete with better insulation properties than traditional concrete is possible while still providing sufficient strength and stiffness for structural use. With a dry density of 1657 kg/m3, 24% porosity, 31.5 MPa compressive strength, 12 GPa E-modulus, and 0.62 W/m·K thermal conductivity, the proposed 3D cork concrete thermally outperforms conventional options. Directional dependency was observed predominantly due to the pressure-induced process, in which compressive, splitting tensile, and flexural strengths and modulus of elasticity displayed distinct anisotropy levels (0.28, 0.15, 0.20, 0.12, respectively). These directional strength dependencies must be accounted for and reflected in the design of overall printed concrete structures. The results provide new insights into the mechanical and thermal behavior of 3D-printed lightweight concrete and validate its printability through curved walls.
Brown clay is an abundant yet underutilized supplementary cementitious material (SCM) due to its low kaolinite content and mixed-clay mineralogy. This study demonstrates that calcined brown clay (CBC) functions as an effective cement replacement when combined with carbon fiber (CF) reinforcement in mortar. CBC (0-30%) and CF (0-0.5%) were evaluated using a 13-run RSM central composite design covering fresh, mechanical, and microstructural responses. XRD indicated CBC's quartz-rich mineralogy, while weaker portlandite reflections at 30% replacement were consistent with dilution and possible pozzolanic reaction. Both CBC levels (15 and 30%) met ASTM C618 strength activity index requirements. The M0.25/15 mix achieved 48.6 MPa compressive strength, with CF reducing the brittleness index from 10.6 to 7.0-7.4, indicating pseudo-ductile behavior. RSM models yielded R2 of 0.94-0.99, with optimal formulation at 0.30% CF and 29.5% CBC. CBC alone reduces carbon intensity 13% below control, positioning strength-normalized carbon intensity as a more meaningful sustainability metric.
This study presents the engineering development and field validation of an automated scanning platform integrating established impact-echo (IE) and ground-penetrating radar (GPR) methods for condition assessment of vehicle-accessible reinforced concrete retaining walls. It was designed for the challenges unique to vertical inspection and refined through iterative prototyping. A coordinated post-processing workflow generated delamination, energy-attenuation, surface-stiffness, and corrosion-indication maps. The system was deployed on reinforced concrete retaining walls and enabled continuous longitudinal scanning over selected 1-m-high wall regions, together with spatially coordinated multimodal inspection outputs. Three deterioration regions were examined: corrosion only, delamination only, and combined corrosion with delamination. Results were validated by ultrasonic tomography and core extraction of reinforcement, supporting the system’s ability to localize and differentiate the selected deterioration conditions within the tested retaining-wall regions. The platform therefore demonstrates feasibility for structural health monitoring of vertical infrastructure, offering reliable multi-sensor assessment to support targeted maintenance and repair.
The placement of lifts and staircases is crucial in built environment layout design. As indoor landmarks, their role in guiding people with different senses of direction (SOD) remains unclear. Five virtual library wayfinding scenes were established based on common lift-staircase combinations. Forty-six participants completed wayfinding tasks in each scene. Excessive distance and pointing error measured wayfinding efficiency and spatial cognition. Results show that under lift placement intervention, SOD trends in wayfinding efficiency differ from those in spatial cognition. High-SOD individuals show better spatial cognition than low-SOD individuals, yet no significant efficiency difference exists between groups. This is attributed to lift layout's strong impact on low-SOD individuals. Placing lifts adjacent to the atrium enhances their spatial cognition and efficiency; orienting lift doors perpendicular to the main entrance further improves efficiency. Adjusting circulation space layout within the built environment can alleviate low-SOD individuals' wayfinding difficulties, thereby enhancing navigation equity in built environments.
To promote the sustainable utilization of silico-manganese slag (SiMnS) as an industrial solid waste and address the research gap in the rheology of alkali-activated SiMnS-based materials, the study was conducted to systematically investigate the effects of NaOH dosage and water-to-binder (w/b) ratio on the rheological behavior, particle morphology and interfacial characteristics of fresh pastes. Particle morphology under flowing conditions was characterized using advanced imaging. Results show that at low w/b ratios (0.35, 0.40), plastic viscosity increases with NaOH dosage; at high w/b ratios (0.45, 0.50), it first decreases and then increases after the NaOH dosage exceeds 6%. Microstructural analysis reveals that alkali activation promotes flocculated structures with increased particle size and reduced circularity, enhancing flow resistance. Zeta potential confirms electrostatic interactions govern particle dispersion and viscosity. These findings provide a theoretical basis for mix design optimization and construction engineering management of alkali-activated SiMnS-based materials.
Wooden façades in multi-storey buildings frequently rely on fire-retardant treatments to meet safety requirements. However, their long-term performance under outdoor exposure is difficult to assess using conventional methods. This study presents a non-destructive monitoring approach based on near-infrared hyperspectral imaging to track deterioration of reaction-to-fire performance in wood treated with a phosphorus-based fire retardant and subjected to leaching cycles. Treated Scots pine samples were exposed to simulated weathering, followed by reaction-to-fire testing using mass-loss calorimetry. Hyperspectral data were combined with canonical partial least squares linear discriminant analysis to discriminate fire performance categories and detect performance transitions as fire retardants were depleted. The model achieved 77% cross-validation and 96.2% test accuracy, and enabled spatial prediction of fire performance across wood surfaces. The results demonstrate that hyperspectral imaging provides a practical tool for monitoring fire-related performance degradation in timber components with phosphorus-based fire retardants, supporting inspection, maintenance, and safety management of façades.
High-fidelity textured three-dimensional (3D) building meshes are essential for digital twins, facade inspection, and condition assessment, but reconstructing fine details such as cracks remains challenging. This paper proposes a region-aware Neural Radiance Field (NeRF) method for reconstructing building meshes. Key innovations include: (1) A region-aware adaptive sampling algorithm that increases sampling density in complex texture areas using a 3D spatial attention field; (2) A decoupled pipeline separating mesh extraction from texture synthesis via learned signed distance fields; and (3) region-guided mesh refinement that enhances geometric detail in critical facade areas. Experiments on UAV-captured building data show better mesh regularity and texture clarity than representative photogrammetric and neural baselines. ROI-based metrics and local geometric-detail indicators provide complementary evidence of improved reconstruction in fine-textured regions. The workflow yields an explicit textured mesh without dense point-cloud meshing after camera pose estimation, supporting offline digital documentation for inspection-oriented building models.
In circular construction, data scarcity hinders both decision-making about reuse potential and the value of real estate. Digital technologies can extract data for the reuse of building components and materials. However, there is limited understanding of how broadly they support data management for the reuse of building elements. This paper examines how digital technologies are currently used to gather, process, store, and share data for building stock reuse. The research was conducted through a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique. The analysis identifies the ways in which digital technologies are used at various interoperability levels and discusses the observed clusters of technologies and material-specific workflows. The findings offer insights into the limitations of current digital workflows used for building data management. These knowledge gaps may inform research into developing digital systems that address the entire data management value chain for building stock reuse. The results are important for built-environment stakeholders interested in using digital technologies in the valuation, reuse, and maintenance of the building stock.
The built environment needs low-carbon cementitious materials that can valorize industrial wastes and store CO2. This study proposes a synergistic carbonation route for carbide slag (CS) and calcined coal gangue (CCG). CCG served as an activated aluminosilicate substrate that redistributed CaCO3 deposition from CS surfaces toward CCG interfaces, reducing localized product-layer accumulation and promoting a more distributed carbonation pathway. XRD, TGA, FTIR, BET, SEM, and EDS results supported enhanced carbonate formation, reduced residual Ca(OH)2 signals, and carbonate enrichment on CCG surfaces. When used as a 20 wt% cement replacement, the carbonated CS-CCG powder promoted hydrate nucleation, improved particle–matrix interfaces, and refined the pore structure. The paste achieved the lowest porosity among the tested pastes, 20.53%, and a 28 d compressive strength of 51.40 MPa. These results demonstrate a feasible dual-waste strategy integrating CO2 mineralization, clinker reduction, and cementitious performance improvement for sustainable construction applications.
Trembling aspen (Populus tremuloides) dimension lumber is a promising product for light wood-frame construction, but its ultimate tensile strength (UTS) remains difficult to predict due to high variability from natural defects. This study developed four ensemble machine learning models, random forest, extreme gradient boosting, gradient boosting regression tree (GBRT), and adaptive boosting, to predict UTS of aspen lumber. The dataset covered 321 specimens with a 38 mm × 89 mm cross section and 12 input features, including grade, geometry, specific gravity, moisture content, knot characteristics, and modulus of elasticity (MOE) from machine stress rating (MSR) and longitudinal stress wave tests, all measurable in sawmills. GBRT achieved the best performance (R2 = 0.718), with the minimum MSR-MOE as the most influential predictor. Against empirical models (R2 = 0.239-0.487) and multivariable linear regression (R2 = 0.585), GBRT improved R2 by 0.133-0.479, supporting industry-oriented AI for individual-specimen UTS prediction and economical material use.
The introduction of new experimental data causes computational loss and reduces robustness of the concrete performance prediction model. To solve the mentioned problems caused by repeatedly tuning parameters and model retraining, this paper proposed an incremental update framework for multi-source low-carbon concrete datasets, integrating a Bayesian-optimization-based conditional tabular generative adversarial network (BO-CTGAN), a stacking ensemble model, adaptive weighting, and an experience replay mechanism. The BO-CTGAN was used to augment the dataset containing 15 feature variables. The quality of the synthetic data was assessed by variable correlations, cumulative distributions, K-S and P-values. Furthermore, four machine learning models optimized by BO were utilized to compare the predictive accuracy of the real data and the synthetic data. Three models were used as weak learners to enhance the ensemble model (SEIM), considering MLP, SVM and RF as the base models and XGB as the meta-model. SEIM was evaluated by comparing multiple statistical indicators (RMSE, R2, MSE). Based on these, from data and model perspectives, the experience replay mechanism and adaptive weights were incorporated into SEIM to construct an incremental update model, which effectively mitigates issues such as repetitive training, catastrophic forgetting, and performance degradation. The synthetic data were simulated as new experimental data input into the model, quantifying the influence of train size, data ratio, and adaptive weights on generalization ability. The results showed that BO-CTGAN can effectively identify the feature distribution of experimental data, with the correlation differences of synthetic data being less than 0.3. SEIM can effectively improve the prediction performance of the base model, with R2, RMSE and MSE values of 0.92, 3.11 and 9.7. The LOSS and change rate of model decrease as the data ratio and train size increase. The model proposed in this paper effectively enhances the predictive performance of multi-source datasets. The repeated training and the potential decrease in robustness caused by the influx of new experimental data can be addressed by incrementally updating. This approach is not only applicable to low-carbon concrete but can also be utilized to improve the predictive performance of other multi-source datasets.
Prefabricated construction requires seamless collaboration among producers, transporters, and assemblers within the prefabricated compontent (PC) supply chain. However, traditional linear collaboration often suffer from poor synchronization between assemblers' requirements and market supply, disrupting efficiency and reducing stakeholder benefits. This paper proposes a lean, platform-oriented collaboration management framework to enhance supply-demand coordination among multiple participants across PC supply chain. A multi-objective optimization model integrated with the Shapley value method is developed to optimize collaboration alliance and profit distribution. It minimizes cross-project construction costs via order splitting, while promoting balanced PC production under low-carbon incentives and just-in-time transportation. To ensure equitable profit allocation, three Shapley value-based profit distribution models are designed, incorporating correction coefficients to account for producers' excess production and assemblers' delivery delays. Sensitivity analysis also conducted to assess how production cost fluctuations impact various parties’ production arrangements and total project performance. A real-world case study validate the proposed mechanism. The profits increases for assemblers, producers, and transporters are 9%, 17%, and 4%, respectively. The model remains robust amid production cost fluctuations, and profit distribution strategies effectively enhances long-term collaboration among participants. It offers a practical solution for synchronizing workflows and balancing benefits in multi-project PC operations, advancing industry efficiency and sustainability.
Underwater bridge inspection is often degraded by environmental interference such as turbidity and illumination distortion, resulting in unreliable defect predictions and potentially misleading decisions. To improve reliability, an uncertainty-aware Bayesian segmentation framework integrating uncertainty quantification and attribution analysis is proposed for underwater bridge defect detection. The framework quantifies epistemic and aleatoric uncertainties to evaluate prediction reliability under underwater interference. An attribution-based interpretation strategy was further developed to identify physical sources of uncertainty and investigate the effects of environmental interference on detection reliability. Experiments on mixed underwater datasets demonstrated that the framework achieved high segmentation MIoU while providing well-calibrated uncertainty estimation. Attribution analysis reveals relationships between environmental interference and defect detection reliability: water turbidity and biological attachments primarily affect exposed rebar detection, whereas rough concrete textures predominantly interfere with spalling identification. The proposed framework enhances uncertainty-aware underwater inspection by providing interpretable reliability information, supporting maintenance decisions based on risk assessment.
Parking infrastructure is often overlooked in net-zero strategies despite its potential as energy-active infrastructure through photovoltaic (PV) integration. This study evaluates the life-cycle environmental performance of solar parking systems while advancing a BIM-informed LCA framework. Three typologies are assessed: an automated parking tower with façade and roof PV, a two-storey concrete parking with a solar canopy, and a five-storey concrete parking combining façade PV and canopy systems. Environmental impacts are evaluated following EN 15978 using a functional unit of one parking space over 50 years. The method integrates parametric BIM modeling, automated OpenLCA-ready inventory generation, and process-based LCA. Results show that PV replacement dominates climate change impacts (47–85%), while operational energy demand amplifies impacts in automated systems. Module D assumptions can reverse human health and water scarcity outcomes. Environmental performance depends on the interaction between structure, PV capacity, and demand, with no single typology performing best.
This study investigates hybrid shear-flexural strengthening strategies for reinforced-concrete T-beams using external reinforcement, i.e., carbon fiber-reinforced polymer (CFRP) sheets and closed-form near-surface-mounted (NSM) CFRP ropes. Large-scale experiments evaluated U-shaped shear externally bonded reinforcement (EBR) systems with and without rope anchorage and closed-form NSM strengthening configurations. Closed-loop ropes and anchored U-shaped sheets suppressed premature shear failure, enabled yielding of the tensile reinforcement, increased load-carrying capacity, and promoted flexure-controlled behavior. An experimentally verified finite element model was further used to investigate the influence of concrete strength on the efficiency of strengthening. The parametric analysis identified a near-optimum efficiency range corresponding to the concrete strength of the tested specimens. The closed-loop NSM rope system was found to be experimentally efficient and theoretically predictable. An analytical model was developed to quantify the shear contribution of closed-loop CFRP ropes, demonstrating the importance of the extended bond-development length provided by the closed-loop configuration.
This study examines Reynolds-number scaling thresholds for reduced-scale modelling of airflow in idealized urban street-canyon arrays. Six variables are considered, namely Reynolds number (Re), dimensionless building height (H′), coefficient of variation of building height (CVH), planar area density (λp), staggering ratio (SG), and wind direction (θ). Flow similarity across scaling ratios is evaluated with the adapted deviation rate (ADR) to derive the critical Reynolds number (Rec) required to achieve scaling threshold. 2,160 computational fluid dynamics (CFD) simulations were conducted. The results suggest that H′, CVH, and λp significantly influence ADR (p ≤ 0.005), while SG and θ are not influential (p > 0.3). A CFD-based generalized linear model (GLM) is derived to relate ADR to the governing variables with high accuracy (R2 = 0.96). The GLM estimates Rec within the investigated parametric space and provides reference for designs of reduced-scale wind-tunnel and CFD studies of comparable idealized urban street-canyon arrays.
