According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.

The sensitivity of simulated flood depth and area to DEM resolution are acknowledged, but their effects on flood damage and risk estimates are less well understood. This study sought to analyse the relative benefits of using global DEMs of different resolution sizes, 5 m AW3D Standard, 12.5 m ALOS PALSAR and 30 m SRTM, to simulate flood inundation, damage and risk. The HEC-RAS 2D model was adopted for flood simulations, and the Toba River in the Klang River Basin in Malaysia was chosen for the case study. Simulated inundation areas from AW3D coincide the most with reported flooded areas, but the coarser-resolution DEMs did capture some of the reported flooded areas. The inundation area increased as the resolution got finer. As a result, AW3D returned almost double flood damage and risk estimates compared to ALOS PALSAR, and almost quadruple compared to SRTM for building-level damage and risk analysis. The findings indicate that a finer-resolution DEM improves inundation modelling and could provide greater flood damage and risk estimates compared to a coarser DEM. However, DEMs of coarser resolution remain useful in data-scarce regions or for large-scale assessments in efforts to manage flood risk.

This study addresses the urgent need for effective water treatment methods by synthesizing and characterizing activated bentonite clay (ABC) adsorbents to remove methylene blue (MB) from aqueous solutions efficiently. Conventional adsorbents often exhibit limitations in efficiency and regeneration capabilities, necessitating novel approaches to water treatment. The primary objective is synthesizing and characterizing high-quality ABC adsorbents capable of effectively removing MB. The activation process was optimized, and adsorbent performance was evaluated regarding MB removal efficiency and regeneration potential. Various activation dos-ages were investigated, and comprehensive physicochemical characterization was performed using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), BET (Brunauer–Emmett–Teller) analysis, X-ray fluorescence (XRF), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The synthesized adsorbents demonstrated exceptional MB removal efficiency (99.593%) and impressive adsorption capacity (22.131 mg/g) when activated with 16 M sodium hydroxide. The adsorption process exhibited spontaneity and exothermicity, as validated by Freundlich and second-order kinetic models. Furthermore, the adsorbents showcased successful regeneration and reusability over three cycles, highlighting their potential for long-term application in water treatment. This study significantly advances water treatment by offering a novel approach to MB removal using base-activated bentonite clay (BABC) adsorbents, contributing to the development of sustainable water treatment solutions.

Embankment dams have many advantages; however, they frequently develop seepage problems which may cause dams’ failure. In this study, comparison analysis of seepage through embankment dams was conducted using three different methods, namely experimental tests, mathematical calculations and numerical modeling. Three homogeneous embankment dam models with different downstream drainage filters were considered. Results revealed that SEEP/W model is inappropriate to compute the water flow volume if there is an intersection between the seepage flow line and the downstream slope of the embankment dam due to the appearance of pipes. Numerical modeling based on SEEP/W software was found to be compatible to the rest physical models. The findings also demonstrated that for all scenarios, both the Casagrande equations and the SEEP/W model produced seepage lines that closely matched the observed seepage lines. These results highlight the significance of managing the seepage line’s location to ensure the stability of embankment dams through the implementation of horizontal drains.