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Combining different types of fibers inside a concrete mixture was revealed to improve the strength properties of cementitious matrices by monitoring crack initiation and propagation. The contribution of hybrid fibers needs to be thoroughly investigated, taking into consideration a variety of parameters such as fibers type and content. In this paper, the impact of integrating hybrid steel-polypropylene fibers on the mechanical properties of the concrete mixture was investigated. Hybrid fiber-reinforced high-strength concrete mixtures were tested for compressive strength, tensile strength, and flexural strength. According to the results of the experiments, the addition of hybrid fibers to the concrete mixture improved the mechanical properties significantly, more than adding just one type of fiber for specimens exposed to room temperature. Using hybrid fibers in the concrete mixture increased compressive, tensile, and flexural strength by approximately 50%, 53%, and 46%, respectively, over just using one type of fiber. Furthermore, results showed that including hybrid fibers into the concrete mixture increased residual compressive strength for specimens exposed to high temperatures. When exposed to temperatures of 200 °C, 400 °C, and 600 °C, the hybrid fiber reinforced concrete specimens maintained 87%, 65%, and 42% of their initial compressive strength, respectively. In comparison, the control specimens, which were devoid of fibers, would be unable to tolerate temperatures beyond 200 °C, and an explosive thermal spalling occurred during the heating process.

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Sugarcane Bagasse Ash (SCBA) is one of the most common types of agricultural waste. By its availability and pozzolanic properties, sugarcane bagasse ash can be utilized as a partial replacement for cement in the production of sustainable concrete. This study experimentally investigated the impact of employing two types of sugarcane bagasse ash as a partial substitute for cement up to 30% on the compressive strength, flexural strength, and Young’s modulus of the concrete mixture. The first type of bagasse ash used was raw SCBA, which was used as it arrived from the plant, with the same characteristics, considering that it was exposed to a temperature of 600 °C in the boilers to generate energy. The second type of bagasse ash utilized, called processed SCBA, was produced by regrinding raw SCBA for an hour and then burning it again for two hours at a temperature of 600 °C. This was done to improve the pozzolanic activity and consequently the mechanical properties of the concrete mixture. The findings indicated that employing raw sugarcane bagasse ash had a detrimental effect on the mechanical characteristics of the concrete mixture but using processed sugarcane bagasse ash at a proportion of no more than 10% had a considerable effect on improving the properties of the concrete mixture. The utilization of processed SCBA up to 10% into the concrete mixture resulted in a 12%, 8%, and 8% increase in compressive strength, flexural strength, and Young’s modulus, respectively, compared to the normal concrete specimen. On the contrary, the inclusion of raw SCBA with varying content into the concrete mixture decreased compressive strength, flexural strength, and Young’s modulus by up to 50%, 30%, and 29%, respectively, compared to the normal concrete specimen. The experimental findings were validated by comparison with ACI predictions. ACI overestimated the flexural strength of SCBA concrete specimens, with a mean coefficient of difference between the ACI equation and experimental results of 22%, however, ACI underestimated the Young’s modulus of SCBA concrete specimens, with a mean coefficient of difference between the ACI equation and experimental results of −6%.

The effect of various parameters on the flexural strength (FS) of ultra-high-performance concrete (UHPC) is an intricate mechanism due to the involvement of several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting the properties of concrete composites due to their better precision than typical regression models. In addition, the developed ML models in the literature for FS of UHPC are minimal, with limited input parameters. Hence, this research aims to predict the FS of UHPC considering extensive input parameters (21) and evaluate each their effect on its strength by applying advanced ML approaches. Consequently, this paper involves the application of ML approaches, i.e., Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict the FS of UHPC. The GB approach is more effective in predicting the FS of UHPC precisely than the SVM and MLP algorithms, as evident from the outcomes of the current study. The ensembled GB model determination coefficient (R2) is 0.91, higher than individual SVM with 0.75 and individual MLP with 0.71. Moreover, the precision of applied models is validated by employing the k-fold cross-validation technique. The validity of algorithms is ensured by statistical means, i.e., mean absolute error and root mean square errors. The exploration of input parameters (raw materials) impact on FS of UHPC is also made with the help of SHAP analysis. It is revealed from the SHAP analysis that the steel fiber content feature has the highest influence on the FS of UHPC.