Search Results (87)

Cement-based building materials usually exhibit weak flexural behavior under high temperature or fire conditions. This paper develops a novel geopolymer with enhanced residual flexural strength, incorporating fly ash/metakaolin precursors and corundum aggregates based on our previous study, and further improves flexural performance using hybrid fibers. The flexural load–deflection response, strength, deformation capacity, toughness and microstructure are investigated by a thermal exposure test, bending test and microstructure observation. The results indicate that the plain geopolymer exhibits a continuously increasing flexural strength from 10 MPa at 20 °C to 25.9 MPa after 1000 °C exposure, attributed to thermally induced further geopolymerization and ceramic-like crystalline phase formation. Incorporating 5% wollastonite fibers results in slightly increased initial and residual flexural strength but comparable peak deflection, toughness and brittle failure. The binary 5% wollastonite and 1% basalt fibers in geopolymer obviously improve residual flexural strength exposed to 400–800 °C. The steel fibers show remarkable reinforcement on flexural behavior at 20–800 °C exposure; however, excessive steel fiber content such as 2% weakens flexural properties after 1000 °C exposure due to severe oxidation deterioration and thermal incompatibility. The wollastonite/basalt/steel fibers exhibit a positive synergistic effect on flexural strength and toughness of geopolymers at 20–600 °C. Full article

Lightweight concrete (LWC) has emerged as a transformative material in sustainable and high-performance construction, driven by innovations in engineered lightweight aggregates, supplementary cementitious materials (SCMs), fiber reinforcements, and geopolymer binders. These advancements have enabled LWC to achieve compressive strengths surpassing 100 MPa while reducing density by up to 30% compared to conventional concrete. Fiber incorporation enhances flexural strength and fracture toughness by 20–40%, concurrently mitigating brittleness and improving ductility. The synergistic interaction between SCMs and lightweight aggregates optimizes matrix densification and interfacial transition zones, curtailing shrinkage and bolstering durability against chemical and environmental aggressors. Integration of recycled and bio-based aggregates substantially diminishes the embodied carbon footprint by approximately 40%—aligning LWC with circular economy principles. Nanomaterials such as nano-silica and carbon nanotubes augment early-age strength development by 25% and refine microstructural integrity. Thermal performance is markedly enhanced through advanced lightweight fillers, including expanded polystyrene and aerogels, achieving up to a 50% reduction in thermal conductivity, thereby facilitating energy-efficient building envelopes. Although challenges persist in cost and workability, the convergence of hybrid fiber systems, optimized mix designs, and sophisticated multi-scale modeling is expanding the applicability of LWC across demanding structural, marine, and prefabricated contexts. In essence, LWC’s holistic development embodies a paradigm shift toward resilient, low-carbon infrastructure, cementing its role as a pivotal material in the evolution of next-generation sustainable construction. Full article

The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na₂SiO₃ ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO₂ emissions, decreasing from 252.09 kg/m³ (GRC rank 1) to 55.0 kg/m³, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R² > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO₂ over 20 years for projects using 1000 m³ annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material. Full article

Cement is widely used as the primary binder in concrete; however, growing environmental concerns and the rapid expansion of the construction industry have highlighted the need for more sustainable alternatives. Geopolymers have emerged as promising eco-friendly binders due to their lower carbon footprint and potential to utilize industrial byproducts. Geopolymer mortar, like other cementitious substances, exhibits brittleness and tensile weakness. Basalt fibers serve as fracture-bridging reinforcements, enhancing flexural and tensile strength by redistributing loads and postponing crack growth. Basalt fibers enhance the energy absorption capacity of the mortar, rendering it less susceptible to abrupt collapse. Basalt fibers have thermal stability up to about 800–1000 °C, rendering them appropriate for geopolymer mortars designed for fire-resistant or high-temperature applications. They assist in preserving structural integrity during heat exposure. Fibers mitigate early-age microcracks resulting from shrinkage, drying, or heat gradients. This results in a more compact and resilient microstructure. Using basalt fibers improves surface abrasion and impact resistance, which is advantageous for industrial flooring or infrastructure applications. Basalt fibers originate from natural volcanic rock, are non-toxic, and possess a minimal ecological imprint, consistent with the sustainability objectives of geopolymer applications. This study investigates the mechanical and thermal performance of a geopolymer mortar composed of metakaolin and red mud as binders, with basalt powder and limestone powder replacing traditional sand. The primary objective was to evaluate the effect of basalt fiber incorporation at varying contents (0.4%, 0.8%, and 1.2% by weight) on the durability and strength of the mortar. Eight different mortar mixes were activated using sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃) solutions. Mechanical properties, including compressive strength, flexural strength, and ultrasonic pulse velocity (UPV), were tested 7 and 28 days before and after exposure to elevated temperatures (200, 400, 600, and 800 °C). The results indicated that basalt fiber significantly enhanced the performance of the geopolymer mortar, particularly at a content of 1.2%. Specimens with 1.2% fiber showed up to 20% improvement in compressive strength and 40% in flexural strength after thermal exposure, attributed to the fiber’s role in microcrack bridging and structural densification. Subsequent research should concentrate on refining fiber type, dose, and dispersion techniques to improve mechanical performance and durability. Examinations of microstructural behavior, long-term durability under environmental settings, and performance following high-temperature exposure are crucial. Furthermore, investigations into hybrid fiber systems, extensive structural applications, and life-cycle evaluations will inform the practical and sustainable implementation in the buildings. Full article

Unfired bricks offer a sustainable alternative to traditional fired bricks by enabling the large-scale reuse of industrial, construction, and municipal wastes while significantly reducing energy consumption and greenhouse gas emissions. This review contributes to eliminating knowledge fragmentation by systematically organising stabiliser technologies, performance metrics, and sustainability indicators across a wide variety of unfired brick systems. It thus provides a coherent reference framework to support further development and industrial translation. Emphasis is placed on the role of stabilisers—including cement, lime, geopolymers, and microbial or bio-based stabilisers—in improving mechanical strength, moisture resistance, and durability. Performance data are analysed in relation to compressive strength, water absorption, drying shrinkage, thermal conductivity, and resistance to freeze–thaw and wet–dry cycles. The findings indicate that properly stabilised unfired bricks can achieve compressive strengths above 20 MPa and water absorption rates below 10%, with notable improvements in insulation and acoustic properties. Additionally, life-cycle comparisons reveal up to 90% reductions in CO₂ emissions and energy use relative to fired clay bricks. Despite technical and environmental advantages, broader adoption remains limited due to standardisation gaps and market unfamiliarity. The paper concludes by highlighting the importance of hybrid stabiliser systems, targeted certification frameworks, and waste valorisation policies to support the transition toward low-carbon, resource-efficient construction practices. Full article

Geopolymer recycled aggregate concrete (GRAC) is an eco-friendly material utilizing industrial byproducts (slag, fly ash) and substituting natural aggregates with recycled aggregates (RA). Incorporating steel-polypropylene hybrid fibers into GRAC to produce hybrid-fiber-reinforced geopolymer recycled aggregate concrete (HFRGRAC) can bridge cracks across multi-scales and multi-levels to synergistically improve its mechanical properties. This paper aims to investigate the mechanical properties of HFRGRAC with the parameters of steel fiber (SF) volume fraction (0%, 0.5%, 1%, 1.5%) and aspect ratio (40, 60, 80), polypropylene fiber (PF) volume fraction (0%, 0.05%, 0.1%, 0.15%), and RA substitution rate (0%, 25%, 50%, 75%, 100%) considered. Twenty groups of HFRGRAC specimens were designed and fabricated to evaluate the compressive splitting tensile strengths and flexural behavior emphasizing failure pattern, load–deflection curve, and toughness. The results indicated that adding SF enhances the specimen ductility, mechanical strength, and flexural toughness, with improvements proportional to SF content and aspect ratio. In contrast, a higher percentage of RA substitution increased fine cracks and reduced mechanical performance. Moreover, the inclusion of PF causes cracks to exhibit a jagged profile while slightly improving the concrete strength. The significant synergistic effect of SF and PF on mechanical properties of GRAC is observed, with SF playing a dominant role due to its high elasticity and crack-bridging capacity. However, the hydrophilic nature of SF combined with the hydrophobic property of PF weakens the bonding of the fiber–matrix interface, which degrades the concrete mechanical properties to some extent. Full article

Geopolymers, as sustainable alternatives to conventional cement, face application limitations due to pronounced drying shrinkage. This study systematically investigates the effects of cement incorporation (0–40%) on the drying shrinkage mitigation and performance evolution of metakaolin-based geopolymers (MKBGs) through multi-scale characterization of mechanical properties, reaction kinetics, and pore structure refinement. Key findings reveal that 10% cement addition optimally reduces drying shrinkage through pore structure densification and elastic modulus enhancement. The cement–geopolymer hybrid system exhibited a distinctive dual-reaction mechanism: cement hydration produced C-S-H gels that refined the pore structure while simultaneously competing with and delaying the geopolymerization kinetics, as demonstrated by the extended duration of the reaction exotherm. However, cement contents exceeding 20% induce detrimental self-desiccation shrinkage, resulting in net shrinkage amplification. Microstructural analysis confirms that the optimal 10% cement dosage achieves synergistic phase evolution, with N-A-S-H and C-S-H gels co-operatively improving mechanical strength and dimensional stability. This work provides quantitative guidelines for designing shrinkage-resistant geopolymer composites through controlled cement hybridization. Full article

Considering the urgent need for sustainable construction materials, this study investigates the mechanical and microstructural responses of novel hybrid geopolymer concrete blends incorporating Fly Ash (FA), Ground Granulated Blast Furnace Slag (GGBS), Cement (C) and Precipitated Silica (PS) as partial replacements for traditional cementitious materials. The motive lies in reducing CO₂ emissions associated with Ordinary Portland Cement (OPC). The main aim of the study was to optimise the proportions of industrial wastes for enhanced performance and sustainability. The geopolymer mixes were activated using a 10 M sodium hydroxide (NaOH)—Sodium Silicate (Na₂SiO₃) solution and cast into cubes (100 mm), cylinders (100 mm × 200 mm) and prism specimens for compressive, split tensile and flexural strength testing, respectively. Six combinations of mixes were studied: FA/C (50:50), GGBS/C (50:50), FA/C/PS (50:40:10), FA/GGBS/PS (50:40:10), GGBS/C (50:50) and GGBS/FA/PS (50:40:10). The results indicated that the blend with 50% FA, 40% GGBS and 10% PS exhibited higher strength. Mixes with GGBS and PS presented a l0 lower slump due to rapid setting and higher water demand, while GGBS-FA-cement mixes indicated better workability. GGBS/C exhibited a 24.6% rise in compressive strength for 7 days, whereas FA/C presented a 31.3% rise at 90 days. GGBS/FA mix indicated a 35.5% strength drop from 28 days to 90 days. SEM and EDS analyses showed that FA-rich mixes had porous microstructures, while GGBS-based mixes formed denser matrices with increased calcium content. Full article

This study explores the fabrication, structural characteristics, and performance of an innovative porous geopolymer membrane made from waste natural zeolite powder for Pb(II) removal, with potential applications in wastewater treatment. A hybrid geopolymer membrane incorporating polyvinyl acetate (PVAc) (10, 20, and 30 wt.%) was synthesized and thermally treated at 300 °C to achieve a controlled porous architecture. Characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), revealed the disappearance of characteristic C=O and C-H stretching bands (~1730 cm⁻¹ and ~2900 cm⁻¹, respectively), confirming the full degradation of PVAc. Thermogravimetric analysis (TG) and differential scanning calorimetry (DSC) indicated a total mass loss of approximately 14.5% for the sample with PVAc 20 wt.%, corresponding to PVAc decomposition and water loss. Energy-dispersive spectroscopy (EDS) elemental mapping showed the absence of carbon residues post-annealing, further validating complete PVAc removal. X-ray diffraction (XRD) provided insight into the crystalline phases of the raw zeolite and geopolymer structure. Once PVAc removal was confirmed, the second phase of characterization assessed the membrane’s mechanical properties and filtration performance. The thermally treated membrane, with a thickness of 2.27 mm, exhibited enhanced mechanical properties, measured with a nano-indenter, showing a hardness of 1.8 GPa and an elastic modulus of 46.7 GPa, indicating improved structural integrity. Scanning electron microscopy (SEM) revealed a well-defined porous network. Filtration performance was evaluated using a laboratory-scale dead-end setup for Pb(II) removal. The optimal PVAc concentration was determined to be 20 wt.%, resulting in a permeation rate of 78.5 L/(m²·h) and an 87% rejection rate at an initial Pb(II) concentration of 50 ppm. With increasing Pb(II) concentrations, the flux rates declined across all membranes, while maximum rejection was achieved at 200 ppm. FTIR and EDS analyses confirmed Pb(II) adsorption onto the zeolite-based geopolymer matrix, with elemental mapping showing a uniform Pb(II) distribution across the membrane surface. The next step is to evaluate the membrane’s performance in a multi-cation water treatment environment, assessing the sorption kinetics and its selectivity and efficiency in removing various heavy metal contaminants from complex wastewater systems. Full article

The growing number of reinforced concrete (RC) structures nearing the end of their service life demands innovative strategies for preservation and retrofitting. Environmental degradation, aging infrastructure, and increased loading demands highlight the need for sustainable, durable, and cost-effective solutions. This paper reviews advancements in preserving and retrofitting RC and concrete infrastructure systems. Innovations include low-carbon binders, supplementary cementitious materials (SCMs), geopolymer concrete, and self-healing technologies to enhance durability and reduce environmental impact. Advanced retrofitting techniques, particularly fiber-reinforced polymer (FRP) systems, modularized steel reinforcement, and hybrid approaches, effectively improve resilience against environmental and operational stresses. Computational tools and machine learning offer promising pathways for optimizing mixture designs and enhancing sustainability. However, critical challenges remain, including scalability issues, performance variability, economic feasibility, and the lack of standardized guidelines. Addressing these challenges will require coordinated efforts across academia, industry, and regulatory bodies to establish performance-based guidelines, develop standardized testing protocols, and conduct comprehensive lifecycle assessments. The findings of this review contribute valuable insights for enhancing infrastructure resilience, reducing environmental impacts, and supporting global sustainability initiatives aimed at achieving net-zero emissions and climate resilience. Full article

Over the past few decades, researchers have focused on developing new compositions and preparation techniques for geopolymers, as multifunctional products, to optimize their characteristics for use in multiple applications. Therefore, this paper investigates metakaolin geopolymer foam and introduces new geopolymer foams based on hybrid metakaolin and wollastonite mineral precursors for water purification. The geopolymer foams were prepared using an alkaline activator, mineral-based powders (wollastonite and metakaolin), a foaming agent (aluminum powder), and a foam stabilizer (olive oil). In addition to mechanical tests and assessments of the adsorption capacity of heavy metal ions, the geopolymer foams were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The geopolymer foams exhibited unique pore structures, containing four classes of pore networks with diameters around 1000 µm, 25 µm, 3 µm, and a well-arranged mesopore network of 50 nm. The utilization of wollastonite (CaSiO₃) alongside metakaolin as a hybrid precursor led to fundamental changes in the composition of the geopolymer binders: a new crystal phase, Ca₅(SiO₄)₂(OH)₂, was formed, and the Si-Al-Na crystal phase disappeared, which led to an increase in the amorphous phase from 87% to 92%. The adsorption rate of heavy metal ions, namely Cr, Co, Cu, Zn, Pd, and As, increased upon introducing wollastonite as a precursor, with absorption rates ranging from 11% to 68%. The findings also revealed that wollastonite significantly increased the geopolymers foams’ compressive strength and elastic modulus from 30 KPa to 67 KPa and from 31 MPa to 126 MPa, respectively. Full article

The construction industry’s evolution towards sustainability necessitates the adoption of environmentally friendly materials and practices. Geopolymer concrete (GeC) stands out as a promising alternative to conventional concrete due to its reduced carbon footprint and potential for cost savings. This study explores the predictive capabilities of soft computing models in estimating the compressive strength of GeC, utilizing multi-layer perceptron (MLP) neural networks and hybrid systems incorporating the Gannet Optimization Algorithm (GOA) and Grey Wolf Optimizer (GWO). A dataset comprising 63 observations from a quarry mine in Malaysia is employed, with influential parameters normalized and utilized for model development. Consequently, we integrate optimization algorithms (GOA and GWO) with MLP to fine-tune the model’s parameters and improve prediction accuracy. The models are evaluated using R², RMSE, and VAF. Various MLP architectures are explored, evaluating transfer functions and training techniques to optimize performance. In addition, hybrid models GOA–MLP and GWO–MLP are developed, with parameters fine-tuned to enhance predictive accuracy. During the training phase, the GWO–MLP model achieved an R² of 0.981, RMSE of 0.962, and VAF of 97.44%, compared to MLP’s R² of 0.95, RMSE of 0.918, and VAF of 94.59%. During the testing phase, GWO–MLP also showed the best performance with an R² of 0.976, RMSE of 1.432, and VAF of 97.51%, outperforming both MLP and GOA–MLP. The GOA–MLP model demonstrated improved performance over MLP with an R² of 0.963, RMSE of 0.811, and VAF of 95.78% in the training phase and R² of 0.944, RMSE of 2.249, and VAF of 92.86% in the testing phase. Hence, the results show that the GWO–MLP model consistently outperforms both MLP and GOA–MLP models. Sensitivity analysis further elucidates the impact of key parameters on compressive strength, aiding in the optimization of GeC formulations for enhanced mechanical properties. Overall, the study underscores the efficacy of machine learning models in predicting GeC compressive strength, offering insights for sustainable construction practices. Full article

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R², VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R² of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R² of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications. Full article

This article’s aim is to analyze physical, mechanical, and fracture properties as well as the thermal investigation of geopolymer composites reinforced with flax, glass fiber, and also the hybrid combination of fibers. Two types of matrices were considered as composites matrices. The first composition was based on fly ash and river sand. The second matrix composition contained fly ash and glass spheres. The content of reinforcement was 1% by mass. Compressive strength and three-point bending fracture tests were performed. The values of fracture toughness and fracture energy were determined. The resonance method was used to verify the dynamic characteristics, such as the dynamic modulus of elasticity and the dynamic Poisson ratio. The results show that single-type fibers in composites based on fly ash and glass spheres did not affect compressive strength. However, introducing hybrid reinforcement increased compressive strength by about 10% compared to the reference specimens. Flax fibers and hybrid reinforcement ensured higher fracture toughness and energy. The results also revealed great potential for glass sphere application to geopolymer materials in terms of fracture mechanics and thermal properties. Despite the lower strength properties in relation to geopolymers based on sand aggregate, applying reinforced fibers into the composite with glass spheres enhanced the compressive strength compared to other materials. Materials modified with glass spheres have a thermal conductivity twice as low as that of materials containing river sand. Full article

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R²) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R² of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices. Full article