Strength Prediction Model Research Articles

Optimization of Filling Material Ratio in Yellow Phosphorus Slag Mine

Yellow phosphorus slag has been considered as a potential cement substitute for mine filling material due to its cementing activity; however, its slow setting and low early strength have limited broader use. This study investigates the grading, compactness, and strength of yellow phosphorus slag combined with tailing sand. Using yellow phosphorus slag as an aggregate, cement as a binder, and mixing tailing sand in different ratios, this study evaluates its feasibility as a coarse aggregate in mine backfill. The key findings are as follows. (1) The grading index of tailing sand was 0.5, aligning with Fuller grading, but it required mixing with coarse aggregates to enhance strength and reduce cement consumption. Yellow phosphorus slag, with a grading index of 0.97, does not match Fuller’s curve and thus benefits from mixing with tailing sand. (2) For mixtures of waste rock and tailings, the 5:5 ratio aligned closely with Fuller’s theory, showing optimal packing density and strength. Mixtures of yellow phosphorus slag and tailings at ratios of 3:7, 4:6, and 5:5 had R2 values of 0.73, 0.80, and 0.85, respectively, confirming reliable fit. The 5:5 mixture provided the best packing density and strength. (3) A new strength prediction model, accounting for aggregate, cement, and water effects, suggests that a 5:5 ratio with a 71% mass concentration and 1/7 ash–sand ratio meets industrial strength requirements. FLAC3D simulations indicated that cemented backfill reduces stress concentrations caused by excavation and supports stability during mining while also absorbing energy through compaction, creating favorable conditions for safe mining operations.

Machine learning-based model for prediction of concrete strength

Machine learning-based model for prediction of concrete strength

Fracture Characteristics and Tensile Strength Prediction of Rock–Concrete Composite Discs Under Radial Compression

To investigate the fracture mechanism of rock–concrete (R–C) systems with an interface crack, Brazilian splitting tests were conducted, with a focus on understanding the influence of the interface crack angle on failure patterns, energy evolution, and RA/AF characteristics. The study addresses a critical issue in rock–concrete structures, particularly how crack propagation differs with varying crack angles, which has direct implications for structural integrity. The experimental results show that the failure paths in R–C disc specimens are highly dependent on the interface crack angle. For crack angles of 0°, 15°, 30°, and 45°, cracks initiate from the tips of the interface crack and propagate toward the loading ends. However, for angles of 60°, 75°, and 90°, crack initiation shifts away from the interface crack tips. The AE parameters RA (rise time/amplitude) and AF (average frequency) were used to characterize different failure patterns, while energy evolution analysis revealed that the highest percentage of energy consumption occurs at a crack angle of 45°, indicating intense microcrack activity. Moreover, a novel tensile strength prediction model, incorporating macro–micro damage interactions caused by both microcracks and macrocracks, was developed to explain the failure mechanisms in R–C specimens under radial compression. The model was validated through experimental results, demonstrating its potential for predicting failure behavior in R–C systems. This study offers insights into the fracture mechanics of R–C structures, advancing the understanding of their failure mechanisms and providing a reliable model for tensile strength prediction.

Study on Strength Prediction and Material Scheme Optimization for Modified Red Mud Based on Artificial Neural Networks

Focusing on the complex nonlinear problems of strength prediction and the material scheme design of modified red mud for use as a road material in engineering applications, a strength prediction neural network is established and utilized to optimize the material scheme, including the compound-solidifying agent ratio, water content, and curing age, based on experimental data accumulated during years of engineering practice and an artificial neural network. In this study, a backpropagation (BP) neural network is adopted, and 114 sets of experimental data are used to train the parameters of the unconfined compressive strength prediction model. Then, using the BP strength prediction model, the material scheme optimization process is carried out, with the strength and material costs as the objectives. The results show that the BP neural network model has a high prediction accuracy, the relative prediction error is basically within 10%, the root-mean-squared error is less than 0.04, and the correlation coefficient is more than 0.99. According to the strength requirements of modified red mud in different road projects and the constraints of each property, an optimal material scheme with a lower cost and higher 7 d target strength is obtained using a mix of polymer agent–fly-ash–cement–speed-cement in a ratio of 0.02%:1.96%:4.78%:0%, with a 33.93% water content of raw red mud, so that the target strength and material cost are 2.987 MPa and 17.099 CNY/T. This study creates an optimal material scheme, incorporating the compound-solidifying agent ratio, curing age, and water content of the modified red mud road material according to the strength requirements of different projects, thereby promoting the popularization of the utilization of red mud with better engineering practicability and economy.

Machine learning driven design of high-performance Al alloys

Aluminum (Al) alloys with both high strength and thermal conductivity (TC) are promising structural materials for wide application across different industries. Yet, design of such alloys is challenging, since strength and TC often share a trade-off. In this paper, we build prediction models for TC and ultimate tensile strength (UTS) of Al alloys using eXtreme gradient boosting (XGBoost) and support vector machine (SVM) algorithms, respectively. The models take physical descriptors from the alloy composition into account. Lasso and Gini Impurity algorithms were adopted for feature engineering. Guided by the models, an Al-2.64Si-0.43Mg-0.10Zn-0.03Cu alloy with TC over 190 W·m-1·K-1 and UTS over 220 MPa was designed. The alloy was fabricated and tested by experiment, and its UTS and TC are close to the model prediction. Microstructure characterization suggests that the fragmented and spherical Si phase, along with a few non-spherical Si phases, may be a key reason for the improved properties.

Shear tests and meso-scale simulation of cold joint structures in rock-filled concrete: Considering the mutual contact effects between component materials

To realistically reflect the shear performance of the rock-filled concrete (RFC) between layers, this study relies on an RFC dam project in Guizhou Province. Test blocks, measuring 5 m × 4 m × 2 m with cold joints, were cast on site and subjected to shear tests. Additionally, a three-dimensional four-phase meso-model was developed, considering the mutual contact effects between component materials. The mechanical response and damage mechanism of the interlayer were analyzed using the cohesive zone model. The results confirm that the cohesive zone model effectively simulates interlayer cold joint structures. The damage modes of the RFC interlayer include cold joint debonding damage (Mode I) and plastic extrusion damage of self-compacting concrete (Mode II). As the exposed height of the rocks increases, the damage mode shifts from Mode I to Mode II, and higher normal stresses intensify Mode II damage. Meanwhile, the paths of crack extension depend on the distribution of shear-resistant rocks, with weak interfacial transition zones serving as “bridges’’ for crack propagation. The interlayer shear strength is positively correlated with the average exposed height of rocks and normal stresses, with the former having a more significant impact. Finally, the accuracy of the proposed shear strength prediction model for the RFC interlayer was validated by comparing it with results from other studies, providing useful references for meso-numerical modeling of RFC.

Experimental Study on Mechanical Properties and Stability of Marine Dredged Mud with Improvement by Waste Steel Slag

As marine-dredged mud and waste steel slag in coastal port cities continue to soar, the traditional treatment method of land stockpiling has caused ecological problems. Thus, it is necessary to find a large-scale resource-comprehensive utilization method for dredged mud and waste steel slag. This study uses waste steel slag and composite solidifying agents (cement, lime, fly ash) to physically and chemically improve marine-dredged mud. The physical improvement effect of the particle size and dosage of waste steel slag was studied by the shear strength test under the effect of freeze–thaw cycle. Then, based on the Box–Behnken design of the response surface method, the interaction effects of the solidifying agent components on the unconfined compressive strength were studied. Then, the water stability under dry–wet cycles and a microscopic mechanism were analyzed by XRD and SEM tests. The results show that the waste steel slag with a dosage of 30% and a particle size of 1.18~2.36 mm has the best improvement. The interaction between cement and lime and lime and fly ash has a significant effect on the linear effect and surface effect of 7d unconfined compressive strength, and the strength increases first and then decreases with the increase in its dosage. For the 14d unconfined compressive strength, only the interaction between cement and lime is still significant. The unconfined compressive strength prediction model is established to optimize the mix ratio of the composite solidifying agent. In the water stability, the water stability coefficients of the 7d and 14d tests are 0.68 and 0.95, respectively, and the volume and mass loss rates are all below 1.5%, showing a good performance in dry–wet resistance and durability. Microscopic mechanism analysis shows that waste steel slag provides an ‘anchoring surface’ as a skeleton, which improves the pore structure of dredged mud, and the hydration products generated by the solidifying agent play a role in filling and cementation. The results of the study can provide an experimental and technical basis for the resource engineering of marine-dredged mud and waste steel slag, helping the construction of green low-carbon and resource-saving ports.

Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications.

Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method

The peak shear strength of a rock joint is an important indicator in rock engineering, such as mining and sloping. Therefore, direct shear tests were conducted using an RDS-200 rock direct shear apparatus, and the related data such as normal stress, roughness, size, normal loading rate, basic friction angle, and JCS were collected. A peak shear strength prediction model for rock joints was established, by which a predicted rock joint peak shear strength can be obtained by inputting the influencing factors. Firstly, the study used the correlation analysis method to find out the correlation coefficient between the above factors and rock joint peak shear strength to provide a reference for factor selection of the peak shear strength prediction model. Then, the JRC-JCS model and four established GA-BP neural network models were studied to identify the most valuable rock joint peak shear strength prediction method. The GA-BP neural network models used a genetic algorithm to optimize the BP neural network with different input factors to predict rock joint peak shear strength, after dividing the selected data into 80% training set and 20% test set. The results show that the error of the JRC-JCS model is a little bigger, with a value of 11.2%, while the errors of the established GA-BP neural network models are smaller than 6%, which indicates that the four established GA-BP neural network models can well fit the relationship between the peak shear strength and selected input factors. Additionally, increasing the factor number of the input layer can effectively improve the prediction accuracy of the GA-BP neural network models, and the prediction accuracy of the GA-BP neural network models will be higher if factors that have higher correlation with the output results are used as input factors.

Experimental studies for evaluation of the removal time of form on concrete using ultrasonic pulse velocity methods

In this study, the form removal time of concrete was evaluated using ultrasonic pulse velocity (UPV). The evaluation was performed based on the form removal strength specified in the American Concrete Institute (ACI), British Standard (BS), and Korean Construction Standards (KCS). Additionally, physical and mechanical properties of the concrete were evaluated from the early stages up to 28 days after casting. Specimens of both plain and lightweight concrete (LWC) were prepared for analysis. For the purpose of evaluating the concrete at various strengths, the water-to-cement (W/C) ratios were established at 0.41 and 0.28, aimed at achieving target compressive strengths of 30 MPa and 60 MPa for plain concrete, respectively. The measurement of mechanical properties was conducted through the assessment of penetration resistance, compressive strength, and ultrasonic pulse velocity (UPV). Models for predicting the strength based on regression analysis were proposed, taking into account the characteristics of the setting times (both initial and final). Additionally, the aggregate cross-section, matrix structure, and crystalline phases were examined using scanning electron microscopy (SEM) and X-ray diffraction (XRD). In the results of the experiments, it was observed that Mortar60 reached both initial and final set quicker than Mortar30, a phenomenon attributed to the higher cement content. Initially, Plain and LWC specimens exhibited comparable trends in compressive strength and ultrasonic pulse velocity (UPV). However, when examining the compressive strength development from 1 to 28 days, a notable difference of approximately 33 % was observed between Plain60 and LWC60. Similarly, differences in UPV ranged from 10.12 % to 11.88 %. The logistic regression model was found to predict the form removal strength more accurately than the exponential model, as indicated by the regression analysis results. When these results were compared with previous strength prediction models, the difficulty in predicting form removal strength became evident, primarily due to the substantial errors present in these models. Analysis using scanning electron microscopy (SEM) and X-ray diffraction (XRD) revealed the presence of hydration products within the lightweight aggregate, which signifies the hydration reaction. These products seemed to enhance the performance of the interfacial transition zone (ITZ) between the aggregate and cement paste. Furthermore, Mortar60 was characterized by larger crystalline hydration products, a denser matrix structure, and higher XRD peaks in comparison to Mortar30.

Splitting behavior of polyvinyl alcohol fiber reinforced iron ore tailings concrete

To promote solid waste reutilization and reduce natural river sand consumption, iron ore tailings (IOT) are expected to be used in concrete production as a potential substitute for river sand under the premise of comparable material properties with ordinary concrete. In this study an IOT concrete reinforced by Polyvinyl Alcohol (PVA) fiber has been proposed and the slump and splitting behavior under various IOT substitution rates and PVA fiber contents has been experimentally investigated. Slump tests reveal that the optimal PVA fiber content is 0.1 % and IOT substitution rate is 60 % from the view of workability. Splitting tensile tests show a monotonous strength growth with PVA fiber content increase. The incorporation of IOT has increased concrete splitting tensile strength but the strength is smaller than that of normal concrete as soon as IOT substitution rates exceed 50 %. A regression model and Back Propagation (BP) neural network model have been adopted to establish splitting tensile strength prediction model. Compared to regression model, the BP model has exhibited better accuracy which is suitable to predict the splitting tensile strength of PVA-IOT concrete with PVA content less than 0.3 %.

Degradation mechanism and strength prediction of aeolian sand concrete under sulphate dry-wet cycles

In this paper, aeolian sand (AS) with different contents (0 %, 25 %, 50 %, 75 % and 100 %) was used as fine aggregate to prepare aeolian sand concrete (ASC). The effects of AS content and dry-wet cycle (DWC) times on the durability of ASC under sulfate (5 % Na2SO4) erosion conditions were investigated by DWC tests. The mass loss rate, compressive strength loss rate and relative dynamic elastic modulus (RDEM) were used as the macroscopic performance evaluation indexes, while the damage degradation mechanism of ASC was revealed by combining the microscopic technical means such as SEM, XRD, TG-DSC and NMR. The results show that the macroscopic properties of ASC under the action of DWC of sulphate all show the first increase and then decrease, as when the dosage of AS is below 50 %, the "bead effect" and "padding effect" of AS can further improve the internal densification of ASC, thus effectively resisting external sulphate erosion. During sulphate erosion, SO42- firstly reacts with cement hydration product Ca(OH)2 to generate erosion products such as Ettringite (AFt) and fills them within the original defects, with short-term rnhancement of macroscopic properties. As the DWC continue, the excessive erosion products accumulated, resulting in the destruction of pore structure and extension through the phenomenon, the matrix compactness is reduced, and the macroscopic properties degraded. The grey entropy correlation analysis yielded that the grey entropy correlation grade of bound fluid saturation, harmless pore percentage and less harmful pore percentage with compressive strength were all above 0.8, and the GM(1,4) strength prediction model established on the basis of the above pore structure parameters has a high prediction accuracy, and the relative errors between the predicted strength values and the actual strength values are within 10 %, and the research results can provide certain theoretical support for the study of the durability of ASC in sulfate erosion environment.

Interpretable machine learning models for predicting probabilistic axial buckling strength of steel circular hollow section members considering discreteness of geometries and material

Steel circular hollow section (CHS) members are widely utilized as axial force-resisting structural members in civil engineering structures. The buckling strength under axial loads is one of the critical parameters to determine the performance of the steel CHS members, which is significantly affected by the discreteness introduced by geometries, material, and initial imperfections. However, the reduction factor employed in the modern design codes (i.e. Chinese codes and EC3) only accounts for the reduction caused by all kinds of discreteness and does not reflect the impacts of every single discreteness and imperfection. To fill the gap, this paper proposed an interpretable machine-learning method to provide the probabilistic axial buckling strength of steel CHS members prediction result in a distribution form with the consideration of detailed discreteness. The model to predict the nominal axial buckling strength of steel CHS members was first developed utilizing ten machine learning algorithms after sufficient numerical simulations, where the numerical model was verified using test results. The artificial neural network (ANN) was selected for developing the prediction model due to its highly reliable performance in testing. The developed ANN models were further interpreted utilizing Shapley Additive exPlanations (SHAP) to determine the interrelationship of different parameters. Then, the probabilistic axial bucking strength prediction model was established based on the developed ANN models, where the Latin hypercube sampling method was applied to address the discreteness of geometries, material, and initial imperfections. The generated probabilistic axial bucking strength prediction model’s effectiveness was verified by the evidence that the machine learning prediction results can highly match the numerical results' probability density function and the result from codes while significantly reducing the computation time. Finally, the design parameters’ impact on the axial buckling strength’s discreteness was evaluated using the global sensitivity analysis (GSA) method. The result shows that the discreteness of design parameters substantially influences the distribution of the axial buckling strength of the steel CHS members and the proposed prediction model can provide an accurate probabilistic distribution prediction.

Optimization of a tensile strength prediction model for compacted ribbons using NIR-HIS analysis

The tensile strength (TS) of compacted ribbon is a critical quality attribute in the roller compaction process that impacts the quality of the finished product. This study investigated the use of Near Infrared Hyperspectral Imaging Spectroscopy (NIR-HIS) technology for predicting TS of compacted ribbons, considering the effects of surface curvature, different spectral preprocessing methods, and variable selection methods on a predictive model based on Partial Least Squares regression (PLSr). The spectral preprocessing methods evaluated were Mean Centering (MC) and Standard Normal Variate (SNV). The variable selection methods were Filter by Regression Coefficient (REG), Variable Importance in Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Genetic Algorithm (GA). The results indicated that curved surfaces had no significant impact on the predictive performance of the model (p-value of 0.39 for RMSEP). The PLSr-CARS method, combined with MC spectral preprocessing, was successful in selecting and reducing the number of wavelengths from 182 to 5, as indicated by high values of R2pred and RPD, and a low RMSEP value (0.97, 5.75, and 7.60%, respectively). An MLR model using the 5 wavelengths was also developed, showing similar performance to the PLSr model. Both the MLR and PLSr models demonstrated high predictive accuracy and reliability. These models can perform well even when developed using only a few wavelengths, leading to significant reductions in processing time and measurement costs, making them valuable tools for quality control in the pharmaceutical industry.

Comparing Machine Learning Regression Models for Early-Age Compressive Strength Prediction of Recycled Aggregate Concrete

A branch of artificial intelligence called machine learning is well-positioned as a prediction method that can take into consideration several influencing factors and complex inter-factor connections. Without being specifically trained to do so, these machine learning models have the ability to generalize, predict, and learn from data. Regression theory is a key topic in statistical modelling and machine learning. The main goal of this study is to compare the performance of several popular machine learning regression models for predicting the early-age compressive strength of concretes made from recycled concrete aggregates from a structure that demolished following the Sivrice-Elazig earthquake on January 24, 2020. Early-age concrete compressive strength is even more crucial due to factors like the fact that there are thousands of newly built structures in the aftermath of the earthquake, the quick manufacturing of these structures, and the completion of the project in the lowest amount of time. Determining the early-age concrete strength with high accuracy and in a useful manner is crucial for this reason. Seven different classical machine learning algorithms were employed in this study to achieve all of these goals. Early-age concrete compressive strength values were considered for 1 and 3 days. The relationship between the experimental results and the predicted outcomes of the employed algorithms was investigated, and a thorough comparison of these intelligent regression algorithms was conducted. Within the scope of sustainable development and circular economy goals, it is thought that this article will make significant contributions to the literature in terms of utilizing these waste materials and determining the early-age compressive strengths of the concretes produced with high accuracy.

Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes

Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes

Investigation on compressive strength and splitting tensile strength of manufactured sand concrete: Machine learning prediction and experimental verification

Due to experimental constraints, traditional strength prediction models for manufactured sand concrete (MSC) exhibit significant limitations and have a narrow range of applicability. To overcome these limitations and enhance prediction accuracy, this paper, based on the widespread application of machine learning in concrete performance research, employed four algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to develop highly accurate and strongly generalizable models for predicting the compressive strength (CS) and splitting tensile strength (STS) ofMSC. The predictive performance of each model was evaluated, the influence laws of various factors were analyzed, and finally, macroscopic experiments were conducted to validate the models' prediction accuracy and generalization ability. The development of these models and the analysis of the influence laws of various factors on the strength of MSC provide a valuable reference for the mix design. The conclusions are as follows: (1) The XGB model demonstrates the highest prediction accuracy and strongest generalization ability for both the CS and STS of MSC, achieving R2 values of 0.98 and 0.94 on the testing set, respectively. (2) The CS and STS of MSC are primarily influenced by four factors: water-binder ratio (W/B), curing age (AGE), cement (C), and coarse aggregate (RI≥0.08869, RI: relative importance), showing a positive correlation with AGE and C, and a negative correlation with W/B. As the fly ash content, stone powder content, maximum particle size of coarse aggregate, and sand ratio increase, the CS and STS initially increase and then decrease. Appropriately increasing the fineness modulus of manufactured sand is beneficial to both CS and STS, while the addition of slag is beneficial to CS and harmful to STS. (3) The prediction accuracy and generalization ability of the XGB model are verified through three sets of macroscopic experiments: C30, C35, and C40. The relative errors of each set's predictions are less than 8 %. (4) A graphical user interface is constructed based on the XGB model, enhancing its practicality.

A systematic framework of constructing surrogate model for slider track peeling strength prediction

A systematic framework of constructing surrogate model for slider track peeling strength prediction

Response Surface Design Models to Predict the Strength of Iron Tailings Stabilized with an Alkali-Activated Cement

Tailing storage facilities are very complex structures whose failure generally leads to catastrophic consequences in terms of casualties, serious environmental impacts on local biodiversity, and disruptions in the mineral supply. For this reason, structures at risk must be reinforced or decommissioned. One possible option is its reinforcement with compacted filtered tailings stabilized with binders. Alkali-activated binders provide a more sustainable solution than ordinary Portland cement but require an optimization of the tailing–binder mixture, which, in some cases, can lead to a substantial experimental effort. Statistical models have been used to reduce the number of those experiments, but a rational design methodology is still lacking. This methodology to define the right mixture for a required strength should consider both the mixture components and in situ conditions. In this paper, response surface methods were used to plan and interpret unconfined compression strength test results on an iron tailing stabilized with alkali-activated binders. It was concluded that the fly ash content was the most important parameter, followed by the liquid content and sodium hydroxide concentration. From the obtained results, several statistical models were defined and compared according to the definition of a strength prediction model based on a mixture index parameter. It was interesting to observe that models with the porosity cement index still provide reasonable adjustment even when different tailings’ water contents are considered.

Robust prediction models for flow and compressive strength of sustainable cement grouts for grouted macadam pavement using RSM

This study investigates the utilization of pumice stone ash in cementitious grouts for grouted macadam pavement, aiming to enhance flowability and compressive strength. Partial replacement of ordinary Portland cement (OPC) with varying percentages (5–20 %) of powdered pumice stone (PPS) ash was explored, alongside adjustments in water-to-cement (w/c) ratios. Experimental assessments, including flowability tests and compressive strength evaluations, were conducted on fresh and hardened grouts. Response surface methodology (RSM) and ANOVA analysis were employed to analyze the relationship between pumice stone ash content, w/c ratio, flow, and compressive strength. The results show that increasing the PPS content from 0 % to 20 % led to an increase in flow time from 23.6 to 34 seconds at a 0.3 w/c ratio, from 9 to 19 seconds at a 0.35 w/c ratio, and from 7 to 14 seconds at a 0.40 w/c ratio: indicating reduced flowability of the grouts. The 7-day compressive strength increased by approximately 2–17 % when 5–15 % of the cement was replaced with PPS, but a reduction occurred at 20 % PPS. Similarly, the 28-day compressive strength increased by about 4–23 % with 5–15 % PPS, while 20 % PPS led to a decrease in strength. The results highlight specific combinations of pumice stone ash content and w/c ratios, particularly 10 % PPS at 0.35 w/c and 15 % PPS at both 0.35 and 0.40 w/c, that meet grouted macadam standards, balancing flowability and strength. Fit statistics demonstrate the reliability of the predictive model. Optimization aims to maximize pumice stone ash content and w/c ratio, with graphical representation indicating an optimal composition at 17.25 % PPS and 0.35 w/c ratio for efficient grouted macadam construction. Additionally, Significant contribution towards sustainability can be achieved by replacing cement with pumice stone ash in pavement construction.