Volume 65, Issue 1

The growing demand for lightweight, durable, and sustainable materials in the aerospace sector motivates the development of high value biocomposites. This work experimentally investigates the potential of pure bast flax fiber reinforced with zirconium dioxide (ZrO 2 ) nanoparticles at varying concentrations (1, 2, and 3 wt%) for light weight helicopter structures. Laminates were fabricated using a hand lay-up compression molding technique and characterized for low-velocity impact resistance, flexural strength, and interlaminar shear strength (ILSS). Further, scanning electron microscopy (SEM) and dynamic mechanical analysis (DMA) were used to characterize the thermo-mechanical performance and failure mechanisms. The key finding is that the 3 wt% ZrO 2 nanocomposite significantly enhanced all critical properties, it demonstrated a 63 % higher maximum impact force (740 N), a 86 % increase in flexural strength, reaching 134 MPa, compared to the 72 MPa pure flax, increased ILSS by 38.2 %, reaching 4.7 MPa, and a 91 % enhancement in the DMA storage modulus (8.46 GPa). These findings demonstrate the strong potential of these nanocomposites for use in demanding, weight-sensitive applications, such as lighter helicopter structures.

In this study, both individual and hybrid effects of industrial wastes (silica fume, fly ash), fibers (glass and basalt) and nanoparticles (nano SiO 2 , nanoclay) mixed with clay at different ratios were investigated in detail at macro and micro levels at different water contents and curing times. The efficiency of stabilization in clay soils was determined using Gray Correlation Analysis (GCA). A predictive model for the stress-strain relationship of stabilized soils with hybrid material was proposed. Results showed negligible changes in dry density and optimum moisture content with silica fume and fly ash, while nano SiO 2 and nanoclay induced significant variations. The unconfined compressive strength (UCS) tests showed a significant increase in soil strength with the addition of nanomaterials, especially after long curing times. The presence of nano SiO 2 and nanoclay in the composite specimens increased the UCS by 87.15 % and 63.83 % before curing, respectively, while it increased to 317.50 % and 219.74 % after the maximum curing time. GCA identified curing time as the most influential factor, followed by silica fume, glass fiber, and nanoclay, due to progressive bonding development. Nanoclay demonstrated superior UCS enhancement compared to nano SiO 2 , underscoring its effectiveness in soil stabilization. The study contributes to the literature to realize the full benefits of these additives to improve soil stability and the UCS performance, and to address waste recycling, increase the strength of subgrade soils under different environmental conditions, and reduce the costs associated with the use of waste.

The inherent brittleness and high-temperature recrystallization tendency of tungsten heavy alloys (WHAs) make balancing high strength and ductility under harsh conditions a major challenge. This study introduces V, Ta, and Re as strengthening elements into W-Ni-Fe-Co alloys by powder metallurgy, producing five alloy compositions (in weight fraction): the baseline sample S1 (W-2.46Ni-1.05Fe-0.7Co), single-doped samples S2 (1V), S3 (2Re), S4 (4Ta), and multi-doped sample S5 (1V-2Re-4Ta). The results show that S1 has the highest density of 98.6 ± 0.61 %; S3 has a peak hardness of 443.27 ± 13.33 HV; and S5 exhibits the best tensile strength and elongation, at 816.35 MPa and 8.92 %, respectively. These performance improvements are attributed to specific strengthening mechanisms: V enhances hardness and ductility through solid solution strengthening in the tungsten lattice and induces lattice distortion; Re promotes grain refinement and the formation of ReW phases, mainly enhancing hardness through grain boundary pinning; and Ta contributes to strength improvement by enriching and achieving second phase strengthening. In S5, the synergistic effect of multi-element doping effectively balances the heterogeneity of grain size and the distribution of the binder phase network, minimizing micropores and optimizing interface bonding, thereby achieving an excellent synergy between strength and ductility and expanding the alloy’s application potential.

This study investigates the compression behavior of circular concrete columns confined with basalt fiber-reinforced polymer (BFRP) stirrups, comparing their performance with traditional steel-reinforced columns. The objective of the study is to assess the impact of the BFRP transverse reinforcements and concrete mix design (CMD) codes on the compressive strength (CS), energy accumulation (G Acc ), fracture energy (G F ), and ductility of RC cylinders. A total of 81 reinforced concrete (RC) cylinders were prepared using three distinct CMD codes and tested under axial compression. The specimens were confined with 6 mm and 8 mm BFRP and steel spirals/ties at varying rib spacings (45 mm, 60 mm, and 90 mm). The experimental results revealed that closer BFRP spiral/tie spacings (45 mm) significantly enhanced the CS by 7–15 % and G Acc by up to 59 % of the columns due to improved confinement effect, compared to those with larger spacings (60 mm and 90 mm). BFRP ties demonstrated superior performance in terms of CS, G Acc , and G F compared to BFRP spirals, particularly at moderate and larger spacings. Finite element model (FEM) simulations validated the experimental results with less than 8 % deviation and demonstrate a high degree of correlation between predicted and observed failure behaviors. The study suggests that BFRP spirals/ties reinforcements with optimal spacing can effectively replace steel in structural applications, offering comparable performance in strength, ductility, and energy absorption. These findings encourage the use of BFRP-based reinforcements in durable, lightweight, and eco-friendly concrete constructions.

The purpose of this research was to synthesize and characterize calcium (Ca) substituted barium titanate (Ba 1-x Ca x TiO 3 ) ceramics. Compositions with calcium substitution levels of x  = 0.0, 0.01, 0.03, 0.05 and 0.07 were prepared using a solid-state reaction method. All compositions were confirmed by X-ray diffraction (XRD) and Rietveld analysis to have a single-phase cubic crystal structure with slight lattice distortions. The optimal grain structure was determined by morphological analysis based on scanning electron microscopy (SEM). The lowest dielectric loss and highest dielectric constant were observed for the composition with x  = 0.03. P-E loop analysis demonstrated a reduction in remanent polarization with increasing Ca content. The highest recoverable energy ( W rec ) and efficiency were obtained for x  = 0.03. These results indicate that the material is suitable for energy storage devices, capacitors and high frequency optoelectronic applications. Through these insights, a pathway to design advanced functional materials for next generation electronic and energy devices is established.

The insufficient skeletal stability of porous asphalt mixtures (PAM) stems critically from the poorly understood dynamic evolution of aggregate gradation during compaction. The differential impacts of distinct compaction methods remain inadequately characterized. This study systematically investigates the effects of Marshall Compaction (MC) and Superpave Gyratory Compaction (SGC) on gradation variation in PAM-13 and PAM-16, incorporating key parameters such as asphalt addition states and compaction energy levels. Specimens were prepared under both with asphalt and without asphalt conditions, subjected to multi-level compaction energy, and subsequently analyzed through sieve analysis. Key findings reveal that MC induces more pronounced coarse aggregate breakage (up to −15.06 %) and fine aggregate generation (up to 101.64 %); SGC better preserves fine aggregate integrity; asphalt provides protective effects predominantly for fine aggregates; and increasing compaction energy continuously exacerbates fine aggregate fragmentation. The findings elucidate the gradation variation patterns induced by different compaction methods, thereby providing a critical theoretical foundation for optimizing the design and construction control of PAM.

The need for energy efficiency and sustainable building materials is increasing today. In this context, new material integrations that aim to improve the thermal and mechanical performance of building elements are being investigated. This study experimentally investigated the effects of two phase change materials (PCM-1(CACI 2 .6H 2 O), PCM-2(NEXTEK24D)) and a thermal insulation material (polyurethane) on the thermal conductivity and compressive strength of hollow bricks. The internal voids of the bricks were filled with polyurethane and two different PCMs to evaluate the contributions of these additives to the mechanical and thermal behavior of the building elements. The findings indicate that adding PCM increases the bricks’ thermal resistance values and reduces internal temperature fluctuations. Bricks filled with polyurethane and FDM showed increased compressive strength, up to 28 % vertically and 74 % horizontally, compared to reference specimens. This emonstrates that filling bricks with such materials improves their compressive strength and thermal insulation. In conclusion, bricks with PCM and polyurethane additives offer significant potential for energy-efficient building solutions, particularly in terms of thermal comfort and insulation. However, given the impact of these additions on mechanical performance, further research is needed to optimize material ratios and enhance applicability without compromising structural safety.

Polystyrene (PS) and its nanocomposites have shown considerable potential as selective barriers for gas permeation, yet the influence of molecular weight (MW) on their gas transport behavior remains insufficiently understood. In this study, three PS samples with ascending MWs (PS1–PS3) and their reduced graphene oxide (rGO) nanocomposites (PS1-G–PS3-G) were prepared via hot pressing. Notably, the water vapor transmission rate (WVTR) exhibited a non-monotonic dependence on MW, increasing from 1.50 g/m 2 ·day (PS1) to 1.59 g/m 2 ·day (PS2), then decreasing to 1.27 g/m 2 ·day (PS3). A similar trend was observed in the nanocomposites, which displayed lower WVTR values (0.54, 0.61, and 0.48 g/m 2 ·day, respectively). In contrast, the oxygen transmission rate (OTR) rose sharply with MW – from 509.41 g/m 2 ·day (PS1) to 1,119.8 g/m 2 ·day (PS2) and further to 4,586.11 g/m 2 ·day (PS3) – while rGO incorporation substantially suppressed oxygen permeability (40.72, 58.09, and 138.32 g/m 2 ·day, respectively). Hydrogen transmission rates (HTR) varied only slightly among the PS samples (830, 842, and 907 cm 2 /(m 2 ·day·0.1 MPa), and the addition of rGO induced minimal changes. Among all samples, PS1 and PS1-G exhibited the most favorable barrier selectivity, combining strong water and oxygen resistance with retained hydrogen permeability. These results demonstrate that MW plays a decisive role in governing gas barrier selectivity and provide design insights for optimizing polymer nanocomposites in advanced barrier applications.

This research explores the synthesis of zinc oxide nanomaterials (ZnO NMs) through the utilization of neem leaf extract, presenting an environmentally conscious and sustainable methodology. The biosynthesized ZnO NMs exhibit dual functionality, showing remarkable efficacy in the photocatalytic degradation of natural organic matter (NOM) as well as antimicrobial activity against drug-resistant microorganisms. Under low-energy UV light, ZnO effectively degraded 92 % of NOM within a span of 180 min, concurrently exhibiting substantial antimicrobial effects against Escherichia coli and Candida albicans , characterized by significant inhibition zones. The research further highlights the application of low-energy UV light to improve the photocatalytic efficiency of ZnO NMs, thereby promoting sustainability and reducing the toxicity of by-products. The characterization of ZnO NMs was conducted employing various techniques, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), ultraviolet-visible spectroscopy (UV–vis), and scanning electron microscopy (SEM). At a concentration of 3 g/l, ZnO accomplished a 92 % reduction in NOM over a period of 180 min, accompanied by a 59 % reduction in organic carbon deposition (OCD). The low levels presence of OCD on the surface after the reaction further confirmed the effectiveness of the process. The findings highlight the promise of neem-extract ZnO NMs as an eco-friendly and adaptable substance for environmental cleanup and antimicrobial uses, demonstrating significant antibacterial efficacy at concentrations of 0.05 mg/ml and 0.1 mg/ml.

The incorporation of fracture mechanics into structural engineering has underscored the importance of fracture energy in understanding crack propagation in quasi-brittle concrete. Nevertheless, conventional laboratory testing remains labor-intensive, time-consuming, and costly. This study applies hybrid machine learning (ML) algorithms, including ANN-GA, SVR-GA, XGB-GA, GBR-GA, and a Stacking Ensemble, to efficiently predict key concrete properties such as initial fracture energy (IFEC), modulus of elasticity (ME), tensile strength (TS), water absorption (WA), and abrasion resistance (AR). Among individual models, IFEC-XGB-GA and IFEC-GBR-GA demonstrated the most stable performance, while the Stacking Ensemble improved overall prediction accuracy and robustness by approximately 5–10  compared with single models. For ME prediction, the ensemble approach reduced bias and variance, although GBR performed better under extreme conditions. TS-ANN-GA exhibited overfitting, whereas TS-Stacking Ensemble achieved the lowest testing errors (MAE0.145, RMSE0.232) and the highest explained variance (≈1.014) and correlation (0.973), confirming the advantages of model aggregation. Moreover, GBR-GA effectively predicted water absorption (R 2 0.998) and classified abrasion resistance with about 84  accuracy, highlighting strong potential for durability evaluation. A graphical user interface (GUI) was developed to integrate these models, providing engineers with a practical, data-driven, and physics-informed tool for accurately predicting the fracture energy of concrete.

The control of α-hemihydrate gypsum (α-HH) crystal morphology has a decisive effect on the strength of gypsum products. This study employed natural gypsum as the precursor material and water vapor as the reaction medium. Six anionic surfactants with chain structures features were selected as crystal modifiers comprising three categories of anionic functional groups (R-SO 4 1− , R-SO 3 1− , R-COO 1− ), each with surfactants of varying carbon chain lengths. The α-hemihydrate gypsum (α-HH) was successfully synthesized under controlled hydrothermal conditions at a saturated steam pressure of 0.4 MPa. It is found that anionic surfactants with chain structures can change the α-HH crystal morphology to a short columnar shape. In terms of the impact on the aspect ratio of a-HH, the R-SO 4 1− group has the best effect, while the worst result is represented by R-COO 1− group. For crystal transformation agents containing the same functional group, the larger the molecular weight, the better effect is caused. According to solubility and MD simulations, in the process of natural gypsum reacting to form α-HH, natural gypsum does not completely dissolve but breaks into small crystals, which are then dehydrated and crystallized to form α-HH. Anionic surfactants with chain structures are adsorbed on the crystal faces perpendicular to the c-axis as the crystal size of α-HH increases, hindering the growth of α-HH crystals along the c-axis and ultimately causing the crystal morphology to become short columnar. This work provides insightful guidance from both experimental and theoretical perspectives for controlling the crystal form of α-HH and preparing high-strength gypsum.

Fiber Reinforced Graphene Nano-Engineered Concrete (FRGNCC) is a high-performance, durable, and sustainable material. FRGNCC enhances strength, crack resistance, and service life while reducing CO 2 emissions and overall production cost, making it ideal for modern resilient structures. FRGNCC compressive strength, production cost, and CO 2 emissions are predicted using advanced hybrid machine-learning (ML) algorithms. Five hybrid ML models were developed using 11 input features: (1) ANN-PSO, (2) KNN-PSO, (3) RF-PSO, (4) SVR-GB, and (5) XGB-PSO. A comprehensive dataset compiled from peer-reviewed sources was used, and the models were evaluated using regression metrics. The XGB-PSO model is the most accurate and reliable in predicting the compressive strength of concrete, achieving a very high coefficient of determination (R 2  = 0.981 in the test data and = 0.991 in the training), with the lowest error indices (MAE = 0.604, RMSE = 0.748). Other models performed relatively poorly. RF-PSO came in second with good predictive ability but an error increase of approximately 3 % compared to XGB-PSO, followed by SVR-GB with a slight bias toward overprediction (+5 % error). KNN-PSO showed greater sensitivity at low and high resistance values, with an error increase of approximately 7 %, while ANN-PSO was the least accurate of the tested models, with an error increase of approximately 10 %. The developed hybrid models demonstrated outstanding predictive capability across mechanical, environmental, and economic aspects of FRGNCCs, where XGB-PSO consistently outperformed all other models, achieving near-perfect accuracy in tensile, flexural, and compressive strength prediction (R 2 up to 0.999) with minimal errors, while sensitivity analysis confirmed that cement content and curing age are the most influential factors in strength. Furthermore, the models accurately predicted production cost and CO 2 emissions (R 2  > 0.96) with very low relative errors (2–3%), highlighting their reliability as robust tools for multi-objective optimization in sustainable concrete design. The ML framework is designed for easy integration into a GUI, enabling engineers and researchers to efficiently estimate mechanical properties, cost, and CO 2 emissions of FRGNCC.

The advancement of self-healing concrete represents a transformative step toward sustainable infrastructure, yet the integration of microbial agents with engineered admixtures under marine conditions remains underexplored. This study investigated the synergistic effects of Bacillus sphaericus and polymeric air-entraining agents (AEAs) on the self-healing performance and durability of high-strength concrete (HSC) exposed to saline environments. Two strains of B. sphaericus (UPMB-10 and ATCC 14577) were evaluated for viability, sporulation, and mineralization potential under varying salinity levels, with ATCC 14577 selected for its superior resistance and CaCO 3 yield. Freeze-dried spores enriched with calcium lactate and urea were incorporated into HSC mixes containing different AEA dosages (2–6%), enabling microbial survival within air-void niches. Concrete specimens were subjected to cyclic curing in both tap water and artificial seawater to simulate fluctuating marine exposure. Healing efficiencies reached up to 71 % in tap water, while seawater immersion produced complete crack closure by 28 days and up to 96 % recovery in water tightness by 56 days. X-ray diffraction revealed mineralogical adaptation in seawater, with polymorphic phases such as aragonite and diopside forming alongside calcite, driven by the presence of Ca 2+ and Mg 2+ ions. These diverse precipitates contributed to enhanced crack sealing compared to the predominantly calcitic deposits in tap water. The findings demonstrate that moderate polymeric AEA dosages preserve structural-grade strength (>100 MPa) while supporting microbial viability and self-healing activity. By linking bacterial metabolism with saline-induced mineral diversity, this study introduces an integrative microbial-admixture framework for designing next-generation self-healing HSC tailored for marine infrastructures. The synergy between optimized bacterial strains and controlled air-void incorporation (by suitable polymeric AEAs) offers a durable, autonomous repair mechanism capable of withstanding aggressive coastal environments.

To address the limited understanding of the influence of different welding speeds on the performance of thin-walled 205C aluminum alloy components fabricated via CMT+P dual-pass forming, a single-variable comparative study was conducted within the speed range of 9.0–10.5 mm s −1 to establish the relations between welding speed and porosity, microstructure, and mechanical properties. Quantitative evaluations of microstructure and properties were performed using XRD, EBSD, TEM, microhardness testing, and transverse and longitudinal tensile specimens. The results indicated that the lowest porosity and optimal overall tensile performance were achieved at a welding speed of 10.0 mm s −1 . At this welding speed, the average transverse tensile strength and elongation reached 277.5 MPa and 13.75 %, respectively, while the corresponding longitudinal values were 262.5 MPa and 10.75 %. The maximum tensile strength of 278 MPa was achieved at 10.0 mm s −1 , and the average elongation was about 2.9 % higher than that at 9.0 mm s −1 . The average microhardness was measured at 73 HV, ranging from 67.8 to 77.1 HV. The microstructure predominantly consisted of fine equiaxed grains, with a grain boundary network dominated by high-angle grain boundaries. As the welding speed increased from 9.0 mm s −1 to 10.5 mm s −1 , the average grain size decreased from 51.73 μm to 34.80 μm. The mean KAM initially increased and then decreased, reaching its lowest value and most concentrated distribution at 10.5 mm s −1 . Although the tensile strength at 9.5 mm s −1 was slightly lower than that at 10.0 mm s −1 , the microhardness distribution was most uniform with the narrowest variation range. The macroscopic texture remains weak across all conditions, indicating a limited influence of welding speed on texture development. Within the investigated process window of 9.0–10.5 mm s −1 , this study provides experimental evidence and a practical reference for selecting welding speed in CMT+P WAAM of 205C aluminum alloy components.

This study investigates the sustainable utilization of Al-Kharj marble dust (MD) as a cement substitute in concrete, evaluating its effects on mechanical properties and environmental impact. Cement was partially replaced with 5–30 % marble dust by weight, and the resulting mixtures were assessed through comprehensive experimental methods including compressive strength (ASTM C39), flexural strength (ASTM C78), tensile strength tests, porosity measurement, and life cycle assessment (LCA) using SimaPro software. The results demonstrate that 15 % marble dust replacement provides the optimal performance, achieving 26.41 MPa compressive strength (+5.4 % vs. control), 14.55 MPa flexural strength (+2.1 %), and 9.44 MPa tensile strength, while reducing porosity by 68 %. Environmental analysis revealed a 34 % reduction in carbon emissions (285 vs. 430 kg CO 2 -eq/m 3 ) and a 30 % decrease in cement cost. The findings establish marble dust as a viable sustainable material that enhances concrete performance while reducing environmental impact, offering a practical solution for waste valorization in construction applications.

Lightweight Strain-Hardening Ultra-High-Performance Concrete (LWSH-UHPC) is a lightweight and durable material that enhances structural efficiency and reduces dead loads in high-rise and bridge structures. This study applies advanced hybrid machine learning (ML) models to predict key properties of LWSH-UHPC, including compressive strength (CS), actual density (DD), theoretical density (TD), material efficiency (ME), production cost, and CO 2 emissions. A total of 248 UHPC datasets were collected from the literature, consisting of 176 conventional and 72 lightweight mixtures. Five hybrid ML models were developed using eight input features: ANFIS-GA, CNN-LSTM, SVM + K-Means, XGBoost + K-Means, and RA-PSO. Among these, ANFIS-GA and CNN-LSTM showed the highest accuracy in predicting compressive strength (80–220 MPa). However, most other models performed less effectively at lower strength levels (<120 MPa). The ANFIS-GA model demonstrated superior overall performance, with excellent results for material efficiency (KGE = 0.986, NSE = 0.974, MAPE = 2.3 %), dry density (NSE > 0.97, R = 0.98), and theoretical density (R 2  = 0.963, MAPE = 1.61 %). Sensitivity analysis identified theoretical density (TD) as the most influential factor affecting compressive strength, with the highest correlation (0.44) and relative importance ( μ  = 0.326 ± 0.028). All models achieved high accuracy in predicting production cost and CO 2 emissions (R 2  = 1.000). CNN-LSTM provided the best cost prediction (RMSE = 0.0113, MAE = 0.0091), while XGBoost + K-Means was most accurate for CO 2 emissions (RMSE ≈ 0.0175). The developed ML framework can be integrated into a graphical user interface (GUI) to help engineers quickly estimate mechanical properties, cost, and CO 2 emissions. An open-access data-sharing platform is being developed to promote the use of LWSH-UHPC in bridge and lightweight structural applications.

The mechanical properties of M50 steel, which is widely used in aerospace engine bearings, are significantly influenced by its heat treatment process. This study systematically investigates the effects of tempering at different temperatures (450 °C, 480 °C, 510 °C, 540 °C, 570 °C, and 600 °C) on the microstructural evolution and dry sliding wear behavior of Composite Shear Flow Casting (CSFC) M50 steel. The microstructures under various heat treatment conditions were characterized using scanning electron microscopy (SEM), Transmission electron microscope (TEM), and energy-dispersive spectroscopy (EDS). The results show that, with increasing tempering temperature, both the average friction coefficient and wear volume of CSFC-M50 steel initially decrease and then increase. The optimal tribological performance is achieved at 510 °C, with an average friction coefficient of 0.555 and a wear volume of 3.17 × 10 7  μm 3 . This trend is attributed to the differing temperature sensitivities of carbide precipitation, which slows down, and carbide coarsening, which accelerates. During the wear process, a dense Cr-rich oxides film forms on the surface via oxidation, acting as a self-lubricating layer that reduces wear and enhances surface resistance. The dominant wear mechanisms include oxidative wear, abrasive wear, and adhesive wear.

This study presents a comprehensive long-term investigation (up to 730 days) of advanced high-performance concrete (HPC, w / b  = 0.25) incorporating a combined system of cement kiln dust (CKD, 5–10 %), boiler slag (BS, 5–10 %), and 0.06 % polypropylene fibers (PPF). Such a multi-component hybrid design has not been previously examined for long-term mechanical and durability performance. Mechanical, non-destructive, and durability tests were supported by SEM/EDX microstructural analysis. The combined incorporation of CKD and BS increased open porosity and water absorption, reducing compressive strength and stiffness, while flexural strength benefited from fiber bridging (22 % at 56 days for 10 % CKD + 10 % BS). Despite lower compressive performance, resistance to salt crystallization improved, particularly for 10 % BS + 5 % CKD. SEM/EDX confirmed higher portlandite content, elevated Ca/Si ratios, and a more porous ITZ at higher CKD/BS levels, explaining macroscale strength losses. A simplified cradle-to-gate CO 2 assessment indicated 80–90 kg CO 2 /m 3 reduction for 10 % cement replacement with CKD and an additional 0.5 kg CO 2 /m 3 benefit from granite substitution by BS. The results demonstrate the long-term performance–sustainability balance and define practical dosage limits for durable, low-carbon HPC containing multiple industrial by-products and fibers.

This study evaluates the feasibility of utilizing spent mushroom waste (SMW) as a sustainable partial replacement for clay in the manufacturing of lightweight, thermally insulating roof tiles. Clay was partially replaced with 0–25 wt% SMW and fired at 1,000, 1,050, 1,100, and 1,150 °C. Increasing SMW content decreased bulk density from 2,204.67 to 1,390.88 kg m −3 (at SMW-25, 1,150 °C) and reduced thermal conductivity from 0.95 to 0.237 W m −1  K −1 , owing to porosity generated by organic matter burnout. The transverse breaking strength declined with SMW but recovered at higher firing temperatures; at 1,150 °C, the TBS values were 3,067, 2,785, 2,276, 1867, 1,150, and 987 N for 0–25 % SMW, respectively. Tiles containing 5–20 % SMW fired at 1,100–1,150 °C met the ASTM C1167 requirements for Grade 2–3 roofing tiles, while achieving substantial reductions in weight and thermal conductivity. Tiles passed the ASTM C1167 water permeability test and showed no efflorescence, as per ASTM C67. Based on density reduction, tiles containing 20 % SMW fired at 1,150 °C showed a 36 % decrease in density relative to conventional tiles. These results demonstrate that SMW enables lightweight, thermally insulating roof tiles that satisfy relevant standards while valorizing an abundant bio-residue.

Extrusion-based 3D concrete printing (3DCP) is emerging as a viable construction method due to its potential to enhance automation, reduce material waste and enable low-carbon construction solutions. However, its practical implementation remains constrained by the need for precise control of fresh-state rheology and early-age structural stability. This study investigates the combined influence of three nanoclays, attapulgite (ATT), bentonite (BEN) and sepiolite (SEP), together with a polycarboxylate ether superplasticizer (SP) on the rheological and mechanical behaviour of sustainable mortars developed for 3DCP-related applications. The binder system incorporates 50 % ground granulated blast furnace slag (GGBS) and 3 % silica fume (SF) to reduce embodied carbon while maintaining performance. A controlled shear-rate protocol was used to quantify static yield stress, dynamic yield stress, thixotropy and plastic viscosity, supported by slump-flow and compressive and flexural strength testing at 7 and 28 days. The results show that all nanoclays significantly enhance structuration and thixotropy, with SEP producing the strongest increase in static yield stress and structural rebuilding. ATT and BEN also improved rheological stability, although with lower intensity. SP dosage strongly governed flow behaviour, with 1 % providing the most favourable balance between flowability and shape stability. Nanoclay-modified mixes exhibited reduced slump flow and increased plastic viscosity relative to the reference. SEP yielded the highest 28-day compressive strength, while flexural strength decreased slightly across all nanoclays. The findings demonstrate that targeted combinations of nanoclay and SP can effectively tailor the rheology of low-carbon mortars, indicating their potential suitability for 3D concrete printing.

Granite powder (GP) has gained attention as a sustainable alternative material in concrete production due to its potential to improve the durability and sustainability. The previous researchers focused on the mechanical properties of concrete and limited studies provided a detailed review on the effects of durability and microstructural characteristics of concrete. Therefore, this review critically examines the influence of GP on the concrete durability and microstructural properties. The findings indicate that GP improves concrete density, reduces water absorption, enhances impermeability, and increases resistance to acid and sulphate attacks. It also lowers the heat of hydration which making it suitable for mass concrete applications. In addition, GP improves the pore structure by reducing capillary porosity and improving packing density. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses indicate that GP promotes calcium silicate hydrate (CSH) gel formation. However, higher percentages of GP restrict CSH gel development, which leads to a looser mix, increased micro-cracking, and higher porosity. The optimal percentage of GP varies depending on factors such as the source of materials, mix design, water-cement ratio, curing time, and environmental conditions, which led to variation in research findings. Finally, the review identifies a research gap and recommends future studies.

Warm mix asphalt (WMA) is a sustainable innovation in road construction that enables bituminous mixtures to be produced and compacted at lower temperatures than traditional hot mix asphalt (HMA). This reduction in temperature significantly decreases greenhouse gas (GHG) emissions and other pollutants, supporting global climate change mitigation efforts. WMA technology enhances energy efficiency while maintaining the performance and durability of road surfaces, offering an environmentally responsible alternative for the construction industry. Its adoption reflects a commitment to reducing the carbon footprint of infrastructure projects. These innovations are anchored in three primary techniques, chemical additives, organic additives, and foaming processes, each of which contributes uniquely to reducing energy consumption while improving pavement performance. Extensive research has revealed the multifaceted advantages of WMA. Mechanically, WMA demonstrates exceptional properties, including enhanced resistance to stripping, fatigue, thermal cracking, and rutting, which collectively contribute to the longevity and resilience of asphalt pavements. Environmentally, the reduced energy demands of WMA production not only reduce emissions but also provide an opportunity to integrate recycled materials and industrial byproducts, further reinforcing its eco-friendly credentials. Economically, the lower production temperatures translate into operational cost savings, although a detailed analysis of long-term cost implications is essential to fully understand its financial viability. This review highlights the historical development, material innovations, and advanced techniques underpinning WMA, providing a thorough evaluation of its rheological and fractured properties. This research aims to support the widespread implementation of WMA in pavement applications. This study also emphasized the critical role of life cycle assessment (LCA) in quantifying the sustainability benefits of WMA mixtures. Moreover, the study explores the emerging trends and challenges in the widespread adoption of WMA, emphasizing the need for robust evaluations of its economic, environmental, and safety aspects. Ultimately, WMA technologies represent a pivotal innovation, offering an integrative solution to modernize the road construction industry while addressing pressing environmental concerns.

This narrative review explores the role of Artificial Intelligence (AI) in advancing the synthesis, optimization, and clinical translation of dental biomaterials. It critically evaluates how AI-driven approaches address existing challenges related to material performance, biocompatibility, and durability, while identifying current research gaps and outlining future perspectives. A comprehensive literature synthesis was conducted through electronic searches in PubMed, Scopus, Web of Science, and Google Scholar. Selected articles encompassed both experimental and computational studies. Case studies were analyzed to illustrate the application of AI techniques, including machine learning (ML) for predicting mechanical properties, deep learning (DL) for microstructural and imaging analysis, and optimization algorithms ( e.g. , genetic algorithms) for materials discovery and formulation enhancement. The findings indicate that ML models can accurately predict mechanical properties ( e.g. , flexural modulus), DL enables automated detection of material degradation, and optimization algorithms accelerate the discovery of advanced biomaterials such as high-entropy ceramics. AI also facilitates the development of personalized, adaptive materials, including pH-responsive and self-healing resin composites. Nonetheless, barriers remain, including data scarcity, limited generalizability, high computational demands, and lack of standardization. AI is poised to redefine the landscape of dental biomaterials by enabling smarter material design, improved performance prediction, and accelerated clinical translation. This review advances the field by integrating emerging computational approaches with practical needs and identifying unresolved challenges that hinder real-world adoption. Ultimately, leveraging AI alongside technologies such as 3D printing and digital twin systems can drive the development of next-generation biomaterials tailored to patient-specific clinical outcomes.

The use of recycled tire rubber, particularly crumb rubber (CR), as a partial fine aggregate replacement offers a sustainable pathway to reduce environmental waste and conserve natural resources, aligning with SDG 9 and SDG 12. This review synthesizes 84 peer-reviewed studies to evaluate the mechanical performance, durability, microstructural characteristics, and practical applicability of crumb rubber concrete (CRC). While CRC typically shows reductions in compressive, tensile, and flexural strengths, stemming from weak ITZ bonding, low stiffness, and increased porosity. It exhibits improved ductility, freeze–thaw resistance, and chemical durability. Empirical polynomial regression models are introduced to quantify strength degradation as a function of CR content, and benchmarking against major design codes indicates that CR levels up to 20 % can satisfy minimum strength requirements for selected exposure classes. Recent enhancement strategies, including RF-RC systems and matrix modifications using sulfate-resistant cement, silica fume, and fly ash, demonstrate quantifiable gains, such as ∼25 % increases in compressive strength, post-crack residual strength improvements from 0.10 MPa to 1.4–3.1 MPa, and ductility enhancements exceeding 70 %. Environmental assessments further show that CRC and multi-waste composite systems can reduce embodied energy by 153–227 MJ/m 3 and CO 2 emissions by 30–135 kg/m 3 , with LCA studies reporting up to 75 % decreases in total environmental impacts. Field-scale investigations confirm CRC’s feasibility, with composite slabs achieving 10–19 % higher peak loads and 17–33 % higher first-crack loads than conventional concrete. Overall, limiting CR content to 10–20 % and employing performance-based mix optimization are recommended to balance mechanical performance, durability, and sustainability in next-generation low-carbon construction.

This investigation aims to examine the behavior of natural and manufactured pozzolans, as well as the properties of blended mortars. Eco-friendly building materials are gaining the attention of researchers worldwide, as they are made from supplementary cementitious materials that alter the fresh and mechanical properties of the blended mortar. Granulated blast furnace slag, silica fume, zeolite powder, metakaolin, clay powder, fly ash, rice husk ash, copper slag, and glass powder have all been used to improve the microstructure, workability, setting time, and strength. The control mixes contain a range of replacement rates (5–50 %) as supplementary cementitious materials compared to the OPC reference mortar. Brick clay powder (5–15 %), Granulated Blast Furnace Slag (5–20 %), Silica Fume (5–25 %), Copper Slag (5–35 %), Metakaolin (5–25 %), Fly Ash (5–30 %), Rice Husk Ash (5–20 %), Zeolite Powder (5–20 %), Ceramic Waste (5–35 %) and Glass Powder (5–10 %) were identified as the beneficial proportion. Moreover, strength increases simultaneously with the volume/index of the big content of all supplementary cementitious materials blends, but beyond the critical level of supplementary cementitious materials mixes, it tends to reduce strength. The changes made by integrating supplementary cementitious materials are significant in terms of both the fresh and hardened properties of blended mortar.

Rutting in asphalt concrete is a widespread phenomenon affecting the strength and overall performance of pavements, especially in regions with intense environmental variabilities and transitions, where excessive temperatures and heavy traffic loads exacerbate pavement deformation. This study reviews and synthesizes research on the causes, consequences, and mitigation strategies for rutting with a particular emphasis on Saudi Arabia’s asphalt pavements. Key factors contributing to rutting, advanced asphalt mixtures, and environmental conditions were extensively discussed. Moreover, the work explores the current pavement design requirements, particularly in Saudi Arabia, highlighting unique policies aimed at addressing rutting issues. Comparisons with other nations with relatively distinct climatic challenges were equally addressed to provide a broader understanding of global exceptional practices. The review also identifies key areas for future research, coupled with the development of superior asphalt mixtures, the incorporation of revolutionary components, and the implementation of tracking technology. By consolidating knowledge on rutting in Saudi Arabia, this review aims to inform and guide ongoing and future efforts to enhance pavement overall performance and durability in the region. In the end, recommendations were proposed based on materials, environmental impact, sustainability, and soil conditions that are essential for effective utilization in Saudi Arabia and other countries in the Arabian Peninsula.

The continuous growth of the global population, coupled with increasingly severe environmental stresses such as droughts, floods, and frosts, has made enhancing crop yields a critical research priority. Smart agriculture, which integrates wearable plant sensors with the Internet of Things (IoT), presents a promising solution. This approach enables real-time monitoring of plant growth dynamics and pattern analysis, supports targeted human intervention to minimize resource waste, and promotes sustainable resource management. Within this framework, resistive strain sensors serve as a foundational technology. This minireview therefore summarizes their recent advances, with a focus on sensing materials, fabrication techniques, and emerging applications in wearable plant sensors.

This study investigated the effect of pre-saturated coconut fibres (CF) on the performance of high-performance concrete (HPC) under standard, membrane, and drying curing conditions. Mechanical and permeability related properties and autogenous shrinkage of HPC mixes were examined. Results indicated that CF proves beneficial for concrete cured in dry conditions, proving their internal curing effect. The tensile strength improved by 21–25 % in CF-reinforced mixes under drying conditions, demonstrating effective crack resistance. Water absorption increased minimally in fibrous mixes under moisture-deficient conditions, while chloride ion penetration was reduced, indicating improvement in durability. Autogenous shrinkage decreased by 18.5–65.2 %, depending on fibre dosage. These findings highlight the potential of pre-saturated CF to improve the performance and durability of HPC, particularly in environments with limited external curing.

The incorporation of nanomaterials in concrete improves mechanical strength, durability, and resistance to environmental effects, presenting a sustainable approach for modern construction. This research employs symbolic regression approaches, namely Gene Expression Programming (GEP) and Multi Expression Programming (MEP), to forecast the compressive strength of nano enhanced (nano TiO 2 and nano SiO 2 ) concretes. The developed models were trained and validated using a comprehensive experimental database and evaluated through multiple statistical metrics. Based on the comparative performance metrics, the MEP model clearly outperformed the GEP model, achieving higher predictive accuracy (R 2  = 0.954), lower error values (RMSE = 5.427 MPa, MAE = 4.596 MPa, MAPE = 10.40 %), and stronger reliability (NSE = 0.953) compared to the GEP model (R 2  = 0.914). Model performance was illustrated through Taylor’s diagram. Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots were used to examine feature importance and interaction effects, showing that concrete age, cement, slag, and nano silica enhance strength, whereas higher water content and fine aggregate proportions reduce it. These results highlight the potential of MEP-based modeling to optimize mix design and promote the sustainable use of nanomaterials and supplementary cementitious materials (SCMs) in concrete, offering valuable guidance for sustainable construction.