The development of nanotechnology has led to the creation of materials with unique properties, and in recent years, numerous attempts have been made to include nanoparticles in concrete in an effort to increase its performance and create concrete with improved qualities. Nanomaterials are typically added to lightweight concrete (LWC) with the goal of improving the composite’s mechanical, microstructure, freshness, and durability qualities. Compressive strength is the most crucial mechanical characteristic for all varieties of concrete composites. For this reason, it is essential to create accurate models for estimating the compressive strength (CS) of LWC to save time, energy, and money. In addition, it provides useful information for planning the construction schedule and indicates when the formwork should be removed. To predict the CS of LWC mixtures made with or without nanomaterials, nine different models were proposed in this study: the gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, and linear relationship model. A total of 2,568 samples were gathered and examined. The most significant factors influencing CS during the modeling process were taken into account as input variables, including the amount of nanomaterials, cement, water-to-binder ratio, density, the content of lightweight aggregates, type of nano, fine and coarse aggregate content, and water. The performance of the suggested models was assessed using a variety of statistical measures, including the coefficient of determination (R ²), scatter index, mean absolute error, and root-mean-squared error (RMSE). The findings showed that, in comparison to other models, the GBT model outperformed the others in predicting the compression strength of LWC mixtures enhanced with nanomaterials. The GBT model produced the best results, with the greatest value of R ² (0.9) and the lowest value of RMSE (5.286). Furthermore, the sensitivity analysis showed that the most important factor influencing the prediction of the CS of LWC enhanced with nanoparticles is the water content.

Nanotechnology has made significant inroads across various sectors, augmenting properties and economic impacts. Its pivotal role extends notably to the realm of construction and building. This study focuses on the tangible consequences of incorporating nano-silica (NS) into lightweight concrete (LWC) and its influence on mechanical attributes. The primary aim is to illustrate how NS impacts the mechanical properties of LWC, specifically its effects on compressive strength, flexural strength, and tensile strength in comparison to conventional LWC. The research encompassed the casting and examination of seven distinct concrete mixtures, including a reference mix, in laboratory settings. The study findings highlight that the utilization of lightweight Iraqi porcelanite stone resulted in a one-third reduction in the weight of standard concrete. Furthermore, the introduction of varying quantities of NS into structural LWC yielded enhancements in compressive, tensile, and flexural strength when contrasted with the reference mix, albeit at the expense of workability. Remarkably, The results showed an introduction of varying quantities of NS into structural LWC yielded enhancements in compressive, tensile, and flexural strength when contrasted with the reference mix, albeit at the expense of workability. The findings demonstrated that when doses of 1, 3, 5, 10, 15, and 20% NS were applied, the rate of three models for determining compressive strength at 90 days old rose by 19, 45, 62, 32, 15, and 37%, respectively. On the other hand, when dosages 1, 3, 5, 10, 15, and 20% were added, the percentage of improvement in tensile strength at 28 days of age was 77, 75, 84, 51, 55, and 53%. Additionally, while employing the same above doses, the bending strength at 28 days of age improved by 141, 140, 171, 115, 114, and 108%, respectively. Remarkably, the results also underscored the sustained efficacy of NS, particularly during the later stages of concrete maturation.