Abstract Geopolymers have emerged as promising alternatives to traditional cement-based composites, offering enhanced sustainability and opportunities for recycling industrial waste. The incorporation of waste materials into the binding matrix of geopolymer concrete not only promotes environmental benefits but also significantly improves the overall performance, including mechanical strength, durability, and microstructural integrity of the matrix. This study explores the impact of incorporating varying dosages of nano-basic oxygen furnace slag (NBOFS) and nano-banded iron formation (NBIF) on the properties of high-performance geopolymer concrete (HPGC) that utilizes waste glass as 50% fine aggregate. The research focuses on evaluating both the fresh and mechanical properties, including compressive strength, splitting tensile strength, modulus of elasticity, and flexural strength. Additionally, this study investigated the transport properties of concrete under aggressive environments, such as resistance to chloride penetration, sulfate attack, and sorptivity. The microstructure was examined using scanning electron microscopy. The results demonstrated that the addition of 3% NBOFS and 2.5% NBIF significantly improved the fresh, mechanical, and transport properties of HPGC. These nanomaterials also enhance the splitting tensile strength, flexural strength, and elastic modulus under highly aggressive environmental conditions. The contribution of these nanomaterials to the strength and durability of concrete is particularly relevant in the construction of both substructures and superstructures. Additionally, geopolymer concrete significantly reduces CO 2 emissions by eliminating the requirement for ordinary Portland cement and promoting the recycling of waste products, contributing to more environmentally friendly construction practices.
Abstract The utilization of waste glass with micro- and nanoparticles in ultra-high-performance concrete (UHPC) has garnered significant interest due to its potential to enhance sustainability and material performance. This study focuses on the implications of integrating microwaste glass (MG) and nanowaste glass in the presence of waste foundry sand and its impact on the properties of UHPC. The particular emphasis of the current work is on compressive strength, tensile strength, sorptivity, and microstructure. It is found that MG enhances compressive strength, decreased tensile strength, reduced sorptivity, and a more compact microstructure. The results indicate that replacing cement with 20% microglass achieves the optimal compressive strength by increasing up to 11.6% at 7 days, 9.5% at 28 days, and 10.18% at 56 days. Nanowaste glass, owing to its increased reactivity and larger surface area, accelerates calcium silicate hydrate formation and improves compressive strength. At the same time, the effective utilization of nanowaste glass improves long-term resilience with an optimum compressive strength at 1.5% replacement ratios of 17.5, 18.9, and 16% at 7, 28, and 56 days, respectively. Splitting tensile strength increased by 16% at 20% MG and 21% at 1.5% nanowaste glass, respectively. Utilizing MG and nanowaste glass in UHPC with waste foundry sand is a promising method for boosting material performance and minimizing environmental impact.
The high demand for sustainable and durable construction materials has exposed the limitations of traditional Portland cement and standard geopolymer concrete, particularly in high-performance structural applications. Conventional geopolymer concrete formulations often fall short in meeting the stringent requirements for mechanical strength, thermal stability, and chemical resistance. To address these challenges, this research explores the use of calcium aluminate cement (CAC) clinker and nano rice husk ash (NRHA) in developing ultra-high-performance geopolymer concrete (UHPGC). Four groups of UHPGC mixtures were prepared by varying the CAC clinker content (0 %, 25 %, 50 %, and 75 %) as a partial sand replacement and NRHA (0 %, 1 %, 2 %, and 3 %) as a replacement for ground granulated blast furnace slag (GBFS). The physical, mechanical, and durability properties of the mixes were systematically evaluated. Results demonstrated that the incorporation of CAC clinker and NRHA significantly improved the compressive, splitting tensile, and flexural strengths. The optimal mix, designated as G3 (50 % clinker, 50 % sand, and 2 % NRHA), achieved a compressive strength of 157 MPa at 91 days, a splitting tensile strength of 11.2 MPa, and a flexural strength of 17.5 MPa at 28 days. Durability tests conducted under elevated temperatures and chemical exposures (including chloride, sulfate, and nitrate solutions) confirmed the enhanced performance of the optimized mix. G3 retained 48–53 % of its residual compressive strength at 800 °C and showed high resistance to sulfate and nitrate attack. G4 (75 % clinker, 3 % NRHA) exhibited superior resistance to chloride penetration. Microstructural analysis revealed the formation of dense C-A-S-H and N-A-S-H gels, which contributed to the improved performance. Overall, this study demonstrates the potential of CAC clinkers and NRHA to produce sustainable, high-performance geopolymer concretes suitable for demanding construction environments.
This study evaluates the performance of foamed geopolymer concrete (FGC) incorporating rigid polyurethane (PU) waste as a partial sand replacement and aluminum powder (AP, 1%) as a foaming agent. The mixtures were based on metakaolin, fly ash, and silica fume. Fresh and hardened properties were assessed, including workability, setting time, density, compressive strength, flexural strength, splitting tensile strength, elastic modulus, water absorption, porosity, gas permeability, and chloride ion penetration. Microstructural characteristics were examined using scanning electron microscopy (SEM). The results show that moderate PU incorporation significantly enhances mechanical performance. The optimal mixture (PU30) achieved a compressive strength of 47.25 MPa at 180 days, representing a 15.6% increase compared to the control. Flexural and splitting tensile strengths improved by 19.9% and 16.7%, respectively, while the elastic modulus increased by 33.8% to 0.95 GPa. These improvements are attributed to enhanced particle packing and more efficient stress transfer within the matrix. In contrast, higher PU contents (>30%) reduced mechanical performance due to increased total porosity and weakened interfacial bonding. Durability-related properties indicated that mixtures PU20–PU30 exhibited reduced permeability and optimized pore structure, characterized by lower pore connectivity. SEM observations confirmed a denser matrix with uniformly distributed pores at optimal PU levels. Additionally, the integration of Random Forest regression with GLCM-based texture analysis demonstrated strong capability in predicting mechanical properties from SEM images. Overall, the combined use of PU waste and AP enables the production of lightweight, structurally efficient, and sustainable FGC with improved mechanical and durability performance.
Abstract This research aims to contribute to the advancement of sustainable construction materials using a new composite of coated plastic waste as sand replacement material. This research assessed the predictive capabilities of Random Forest (RF), Particle Swarm Optimization-Support Vector Regression (PSO-SVR), and a Genetic Algorithm Optimized Artificial Neural Network (GA-ANN) that enable accurate, data-efficient prediction of compressive strength in plastic-waste foamed concrete, reducing experimental overhead and guiding sustainable mix optimization to forecast the compressive strength of foam concrete containing plastic waste. The models were evaluated using R 2 metrics, where RF scored 0.9872 and 0.9005, and GA-ANN scored 0.9979 and 0.8853 for the training and testing sets, respectively. Sensitivity analyses of the RF and GA-ANN models were conducted to evaluate the compressive strength of the foam concrete and the impact of each associated input parameter. The findings confirmed that both models accurately predicted the compressive strength of the material. The R 2 values for both models were calculated: for RF 0.9872 and 0.9005, and for GA-ANN 0.9979 and 0.8853. Sensitivity analysis indicated that the highest Permutation Importance values for cement, foam, sand, water-to-cement ratio, and plastic waste were 0.39, 0.34, 0.17, 0.11, and 0.39, respectively. In the GA-ANN case, the greatest Permutation Importance Values of 0.41, 0.31, 0.13, 0.11, and 0.05 were assigned to cement, sand, water-to-cement ratio, foam, and plastic waste, respectively, in that order concerning compressive strength. The PSO-SVR model in green maintained a good balance (AUC = 0.97 in training and AUC = 0.93) in testing. The PSO-SVR model achieved an average performance between those of the other two models. The MAE value was approximately 1.5 in training and 2.8 in testing, whereas the RMSE value was in the range of 4.5–5.0. The results showed the practicality of AI-based frameworks in the focus optimization of mix design and multi-criteria prediction of performance metrics of sustainable foam concrete containing recycled plastic waste.
Concrete, foundational in modern construction, grapples with environmental concerns and performance limitations. Responding to this, this review unravels the potential of graphene-infused concrete. The techniques of integrating graphene into concrete, targeting the discovery of groundbreaking construction materials were systematically probe. Some studies highlight remarkable improvements: adding 0.03% graphene oxide (GO) bolstered concrete's flexural strength by over 40%, and a concentration of 0.05% GO optimized flexural strength by roughly 35%. Impressively, some studies showcased an elevation of up to 79.5% in flexural strength. Notably, incorporating just 0.01-0.1% graphene by weight can amplify compressive strength, flexural strength, and elastic modulus by over 100%. Parallelly, the environmental footprints of graphene's synthesis and its fusion into concrete, illuminating prospects for significant carbon emission curtailments and enhanced sustainability were scrutinized. On the economic front, while graphene concrete currently costs 5-10 times more than its conventional counterpart, its superior attributes suggest promising long-term cost benefits. Conclusively, this review findings spotlight graphene-augmented concrete as pivotal for a sustainable, economically sound, and high-caliber construction future.
Abstract Currently, there is a lack of research comparing the efficacy of machine learning and response surface methods in predicting flexural strength of Concrete with Eggshell and Glass Powders. This research aims to predict and simulate the flexural strengths of concrete that replaces cement and fine aggregate with waste materials such as eggshell powder (ESP) and waste glass powder (WGP). The response surface methodology (RSM) and artificial neural network (ANN) techniques are used. A dataset comprising previously published research was used to assess predictive and generalization abilities of the ANN and RSM. A total of 225 research article samples were collected and split into three subsets for model development: 70% for training (157 samples), 15% for validation (34 samples), and 15% for testing (34 samples). ANN used seven independent variables to model and improve the model, whereas RSM used three variables (cement, WGP, and ESP) to improve the model. The k -fold cross-validation validated the generalizability of the model, and the statistical metrics demonstrated favorable outcomes. Both ANN and RSM techniques are effective instruments for predicting flexural strength, according to the statistical results, which include the mean squared error, determination coefficient ( R 2 ), and adjusted coefficient ( R 2 adj). RSM was able to achieve an R 2 of 0.7532 for flexural strength, whereas the accuracy of the results for ANN was 0.956 for flexural strength. Moreover, the correlation between the ANN and RSM models and the experimental data was high. However, the ANN model exhibited superior accuracy.
Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.
Abstract Eggshell powder (ESP) and date palm ash (DPA) are increasingly used as sustainable cement substitutes in cementitious composites. This study used multi-expression programming (MEP) to develop prediction models due to its advantage of yielding model equations. The attributes of ESP and DPA-modified concrete chosen for modeling include compressive strength (C-S), eco-strength (E-C-S), and cost-strength ratio (C-S-R). Hyperparameters in MEP were fine-tuned to get the maximum accuracy for predictions. The models were validated using R 2 and statistical checks and analyzing the variance among predictions and real values. The MEP models were noted to be exact in estimating C-S, C-S-R, and E-C-S with an R 2 of 95, 93, and 92%, respectively, indicating good agreement with actual data. Additionally, the ±20% index analysis indicated that all values fall within the acceptable range, validating the model’s reliability. The mathematical expression-based MEP prediction models developed in this study can be applied to future C-S, C-S-R, and E-C-S predictions in ESP–DPA-modified concrete. These models are designed to operate with a predetermined set of input parameters and are incompatible with a variable set of inputs. Additionally, it is imperative to maintain consistency in the units of inputs to obtain precise predictions from the constructed models.
