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Traditional building materials have significant limitations in function and performance: insulation materials are easy to peel and age, waterproof materials have a short life, and fireproof materials have degraded flame retardancy. These shortcomings cannot meet the needs of modern buildings for energy efficiency, safety and durability. Therefore, it is imperative to study gradient building materials that integrate function and structure. In this study, based on the non-Fourier heat conduction law, a heat wave propagation model is established to derive a complete analytical solution for the heat wave scattering field of a subsurface circular defect in an exponentially gradient material. The effects of thermal diffusion length ( µ / a ), wave number ( ka ), non-uniformity coefficient ( σ ₁ a ), and defect embedding ratio ( b / a ) on the surface temperature distribution are systematically analysed by the wavefunction expansion method and the virtual mirror technique combined with the independently developed numerical procedure. The results show that: the peak temperature amplitude occurs in the region directly in front of the scatterer; the thermal fluctuation effect is significantly enhanced with the increase of the thermal diffusion length or the decrease of the defect size; the temperature fluctuation response is strengthened by the high modulation frequency (large ka ) and the shallow burial depth of the defects; and the increase of the non-uniformity parameter of the material σ ₁ a results in the increase of the surface temperature. The study confirms the limitations of traditional Fourier’s law in short-pulse heat conduction scenarios, and the results provide theoretical basis and data support for the design of functional gradient materials and nondestructive inspection by infrared thermography.

This paper investigates the significance of thermal diffusion on chloride diffusion in concrete under high ambient temperature in arid climates. Of particular interest is to study the effects of silica fume (SF) and ground granulated blast furnace slag (GGBS) on chloride penetration into concrete subjected to temperature gradient conditions. This was achieved by making three sets of concrete samples—the control samples, the samples containing 5% SF, and the samples containing 5% SF and 50% GGBS. These samples were exposed to a NaCl environment under isothermal and thermal gradient conditions. The total and free chloride contents of the exposed samples were determined via potentiometric titration. The total chloride concentration of the samples exposed to thermal gradient conditions could be 1.3–6 times higher than those exposed to isothermal conditions at the same temperature. The addition of SF and GGBS yielded significantly lower total and free chloride contents than the control samples under isothermal and thermal gradient conditions. While thermal gradient significantly reduces the chloride binding capacity, adding SF and GGBS increases this ability. SEM analysis revealed microstructural changes in concrete due to high temperature and thermal gradients, with larger and deeper pores in samples exposed to thermal gradient. Numerical estimation of chloride concentration and the corrosion initiation time of a reactor containment building was also performed using the modified chloride diffusion equation, including the effects of mass- and thermo-diffusion.

The paper deals with the possibility of using Phase Change Materials (PCM) in concretes and geopolymer composites. The article presents the most important properties of PCM materials, their types, and their characteristics. A review of the latest research results related to their use in geopolymer materials is presented. The benefits of using PCM in building materials include the improvement of thermal comfort inside the building, and also the fact that the additive in the form of PCM reduces thermal gradients and unifies the temperature inside the concrete mix, which can reduce the risk of cracking. The paper also presents a critical analysis related to the feasibility of mass scale implementations of such composites. It was found that the use of PCM in sustainable construction is necessary and inevitable, and will bring a number of benefits, but it still requires large financial resources and time for more comprehensive research. Despite the fact that PCM materials have been known for many years, it is necessary to refine their form to very stable phases that can be used in general construction as well as to develop them in a cost-effective form. The selection of these materials should also be based on the knowledge of the matrix material.

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods.

A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).

3D printing of ceramics has started gaining traction in architecture over the past decades. However, many existing paste-based extrusion techniques have not yet been adapted or made feasible in ceramics. A notable example is coextrusion, a common approach to extruding multiple materials simultaneously when 3D-printing thermoplastics or concrete. In this study, coextrusion was utilized to enable multi-material 3D printing of ceramic elements, aiming to achieve functionally graded porosities at an architectural scale. The research presented in this paper was carried out in two consecutive phases: (1) The development of hardware components, such as distinct material mixtures and a dual extruder setup including a custom nozzle, along with software environments suitable for printing gradient materials. (2) Material experiments including material testing and the production of exemplary prototypes. Among the various potential applications discussed, the developed coextrusion method for clay-based composites was utilized to fabricate ceramic objects with varying material properties. This was achieved by introducing a combustible as a variable additive while printing, resulting in a gradient porosity in the object after firing. The research’s originality can be summarized as the development of clay-based material mixtures encompassing porosity agents for 3D printing, along with comprehensive material-specific printing parameter settings for various compositions, which collectively enable the successful creation of functionally graded architectural building elements. These studies are expected to broaden the scope of 3D-printed clay in architecture, as it allows for performance optimization in terms of structural performance, insulation, humidity regulation, water absorption and acoustics.

High-entropy alloys (HEAs) are increasingly preferred as structural materials in nuclear engineering and aerospace applications. These fields often require the design of dissimilar joints. Here, gas tungsten arc welding (GTAW) was used for the first time to join CoCrFeMnNi HEAs with 316 stainless steel. Microstructural characterization, including electron microscopy, high-energy synchrotron X-ray diffraction, and thermodynamic calculations, along with micro- and macroscale mechanical assessments, was utilized. These methods were instrumental in evaluating and clarifying the effects of the non-equilibrium solidification and weld thermal cycle on the microstructure evolution of the joint. In the fusion zone (FZ), distinctive peninsula-shaped macroscopic segregation area is observed, with its formation being related to the liquidus temperature differences between the base materials (BMs) and the welded metal, compounded by the Marangoni effect. The weld thermal cycle was found to promote multiple solid-state phase transformations in the heat-affected zone (HAZ) adjacent to the CoCrFeMnNi BM, leading to varying degrees of softening. The HAZ near the 316 stainless steel BM maintained its original microstructural and mechanical properties. Fracture predominantly occurred in the FZ, mainly due to the interplay of large columnar grains, macrosegregation effects, and emergence of BCC and σ brittle phases due to the complex chemistry within this region. Thermodynamic modeling validated the formation of these phases. The ultimate tensile strength and elongation at room temperature were approximately ≈493 MPa and ≈10.70%, respectively.

The envelope structure with high light transmittance accounts for an increasing proportion of building energy consumption, which is one of the shortcomings of energy conservation and emission reduction. Cellulose-based aerogel has become a research topic of interest because of its low thermal conductivity and good mechanical properties. However, most cellulose-based aerogels are opaque and flammable limiting their applications. Herein, cellulose/silica composite aerogels (CAS) with \"organic–inorganic\" structures were fabricated by two-step sol–gel method, spin-coating technique and supercritical CO2 drying, using the ionic liquid 1-allyl 3-methylimidazolium chloride salt to dissolve the Cotton pulp, followed by the addition of tetraethylorthosilicate (TEOS) and methyltriethoxysilane (MTES) co-precursors into the cellulose gels. The synthesis mechanism, microstructure, mechanical and thermal properties of as-prepared aerogels samples were investigated. The obtained CAS have low density (0.093–0.170 g/cm3), high specific surface area (660.87–1089.70 m2/g), and high mechanical property (compressive strength of 18.74 MPa, tensile strength as high as 1.54 MPa, and bending tests above 500 times). In particular, the CAS4 shows the lowest thermal conductivity (0.0188 W·m−1·K−1), good thermal stability (> 331 °C), high transparency (91.7%) and excellent flame retardancy. In addition, the self-designed aerogels glasses model was placed in a real outdoor environment for 5 h. The results showed that the temperature difference between the inside and outside of the aerogels glasses model was as high as 12 ℃ under the thermal equilibrium state. Thus, the as-prepared high-performance cellulose/silica composite aerogels may increase the role of aerogels glasses in the building envelope and have promising applications in transparent energy-efficient construction and thermal insulation.

Additive manufacturing (AM) has made rapid progress in most industries; however, the construction sector lags behind, despite substantial potential for growth. This study aims to evaluate recent innovations in AM, with a focus on multi-material additive manufacturing (MMAM), to identify transferable knowledge and technologies for the construction industry. A systematic Boolean search reviewing the Scopus and Web of Science databases identified 33 relevant articles out of 368 papers published in English over the last five years. Material properties, manufacturing processes, and design approaches were collectively identified as key interdisciplinary factors; these included thermal and mechanical property gradation techniques from materials science, multi-scale optimization approaches from engineering, and real-time monitoring systems from manufacturing, which are each transferable to architectural applications. Bibliometric analysis demonstrated growing research trajectories in AI-driven optimization methods and functionally graded materials that could bridge the implementation gap in construction. This article identifies significant knowledge gaps in scaling laboratory-proven MMAM techniques to architectural applications, including material interface challenges, environmental durability concerns, and the absence of design tools specific to building-scale components. We provide a critical roadmap for researchers that prioritizes the development of integrated optimization frameworks; multiscale modeling techniques; novel material combinations suitable for construction environments; and standardized protocol bases for Equipment Design, Process Control, Design Integration, Digital Tools, and Materials Research for evaluating the long-term performance and safety of MMAM building components.

The contribution of shear resisted by flanges of T-beams is usually ignored in the shear design models even though it was proven by many experimental studies that the shear strength of T-beams is higher than that of equivalent rectangular cross-sections. Ignoring such a contribution result in a very conservative and uneconomical design. Therefore, the aim of this research is to investigate the capability of machine learning (ML) techniques to predict the shear capacity of reinforced concrete T-beams (RCTBs) by incorporating the contribution of the flange. Five machine learning (ML) techniques, which are the Decision Tree (DT), Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost), are trained and tested using 360 sets of data collected from experimental studies. Among the various machine learning models evaluated, the XGBoost model demonstrated exceptional reliability and precision, achieving an R-squared value of 99.10%. The SHapley Additive exPlanations (SHAP) approach is utilized to identify the most influential input features affecting the predicted shear capacity of RCTBs. The SHAP results indicate that the shear span-to-depth ratio (a/d) has the most significant effect on the shear capacity of RCTBs, followed by the ratio of shear reinforcement multiplied by the yield strength of shear reinforcement (ρvfyv), flange thickness (hf), and flange width (bf). The accuracy of the XGBoost model in predicting the shear capacity of RCTBs is compared with established codes of practice (ACI 318-19, BS 8110-1:1997, EN 1992-1-2, CSA23.3-04) and existing formulas from researchers. This comparison reinforces the superior reliability and accuracy of the machine learning approach compared to traditional methods. Furthermore, a user-friendly interface platform is developed, effectively simplifying the implementation of the proposed machine-learning model. The reliability analysis is performed to determine the value of the resistance reduction factor (ϕ) that will achieve a target reliability index (βT= 3.5).