Nowadays, using advanced structural materials such as preplaced aggregate concrete (PAC) and fiber-reinforced preplaced aggregate concrete (FR-PAC) are widely investigated due to their benefits in designing infrastructures. Therefore, finding the mechanical characteristics of PAC and FR-PAC can be help structural engineers. This study explores the material characteristics, performance, and potential challenges associated with using PAC and FR-PAC, aiming to provide insights into their practical implementation and long-term benefits in construction. In addition, a superior estimation tool based on multi-target stacked machine-learning (ML) model was introduced to reduce the cost of experimental tests and increase the accuracy and speed of finding the best mixture for PAC and FR-PAC. Experimental tests were conducted to prepare unseen dataset to validate the general ability of the ML models. Results show that the proposed multi-target stacked ML models can estimate the compressive and tensile strengths of PAC specimens with an accuracy of 97.4% and 94.7%, respectively; however, for compressive, flexural, and tensile strengths FR-PAC specimens, the accuracy of 97.7%, 98.0% and 98.3%, were determined, respectively. The proposed predictive model was turned into a graphical user interface (GUI) with ability on predicting the mechanical properties of PAC and FR-PAC in different curing day, and updating the database in future.

This study proposes a novel approach by adding Portland limestone cement (PLC) to preplaced aggregate steel fiber reinforced concrete (PASFRC) to create a sustainable concrete that minimizes CO2 emissions and cement manufacturing energy usage. The method involves injected a flowable grout after premixing and preplacing steel-fibers and aggregates in the formwork. This study evaluates the mechanical properties of a novel sustainable concrete that uses PLC and steel fibers. To achieve the intended objective, long and short end-hooked steel fibers of 1%, 2%, 3%, and 6% were incorporated in PASFRC. Also, Analysis of variance (ANOVA) was used to examine the data. Results indicated that PLC and higher fiber doses increased the mechanical properties of PAC. At 90 days, PASFRC mixtures containing 6% long steel fibers demonstrated superior compressive, tensile, and flexural strengths, registering the highest values of 49.8 MPa, 7.7 MPa, and 10.9 MPa, respectively and differed by 188%, 166%, and 290%, respectively from fiberless PAC. The study confirmed the suitability and effectiveness of using PLC with steel fibers in PAC which significantly improved the mechanical properties of PASFRC. This was verified through analytical analysis and new empirical equations were proposed to predict the mechanical properties of PASFRC.