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The construction of metro tunnels in response to global urban traffic congestion presents challenges such as face stability, excessive cutter wear, cutter head clogging, and groundwater ingress during excavation. Surface settlement (SS) resulting from tunneling is a common issue that can lead to cracks in existing structures. Existing empirical and numerical solutions have limitations, including low accuracy and time-consuming calculations. To overcome these challenges, this study proposes the use of a combination of well-established theories and machine learning (ML), specifically theory-guided machine learning (TGML), to predict SS caused by twin tunnels. The TGML strategy incorporates data verification from a case study in Malaysia and data generation from numerical analysis. Within the realm of SS, tree-based techniques—including random forest, adaptive boost, gradient boosting tree, extreme gradient boost, light gradient boosting, and categorical boosting (CatBoost)—were employed in this study. The tree-based ML algorithms consistently demonstrated robust predictive performance, with coefficient of determination (R2) values consistently exceeding 0.9 and low root mean square error (RMSE) and mean absolute error (MAE) with high variance accounted for (VAF). Among these algorithms, CatBoost exhibited the highest R2 showcasing the lowest errors and highest explanatory power on both training and testing data which outperformed its counterparts. This observation highlights CatBoost's superior predictive capabilities compared to other tree-based algorithms, emphasizing its efficiency in capturing underlying patterns in the data set. TGML emerges as an effective, easy-to-apply, and practical solution for predicting SS induced by twin tunnels in future projects. [ABSTRACT FROM AUTHOR]

Lightweight aggregate concrete (LWAC) is gaining interest due to its reduced weight, high strength, and durability while being cost-effective. This research proposes a method to design an LWAC by integrating coconut shell (CS) as coarse lightweight aggregate and a high volume of wet-grinded ultrafine ground granulated blast furnace slag (UGGBS). To optimize the mix design of LWAC, a particle packing model was employed. A comparative analysis was conducted between normal-weight concrete (M40) and the optimized LWAC reinforced with basalt fibers (BF). The parameters analyzed include CO2 emissions, density, surface crack conditions, water absorption and porosity, sorptivity, and compressive and flexural strength. The optimal design was determined using the packing density method. Also, the impact of BF was investigated at varying levels (0%, 0.15%, and 1%). The results revealed that the incorporation of UGGBS had a substantial enhancement to the mechanical properties of LWAC when BF and CS were incorporated. As a significant finding of this research, a grade 30 LWAC with demolded density of 1864 kg/m3 containing only 284 kg/m3 cement was developed. The LWAC with high-volume UGGBS and BF had the minimum CO2 emissions at 390.9 kg/t, marking a reduction of about 31.6% compared to conventional M40-grade concrete. This research presents an introductory approach to sustainable, environmentally friendly, high-strength, and low-density concrete production by using packing density optimization, thereby contributing to both environmental conservation and structural outcomes. [ABSTRACT FROM AUTHOR]