The necessary application of sustainable engineering methodologies has been increasing as the number of environmental hazards caused by global warming is on the rise. Cement as a traditional common additive for soil improvement has several negative impacts on the environment. This led to an urge for alternative sustainable solutions. The use of biopolymers as environmentally friendly materials is one of the potential options. This study aims to investigate the effect of xanthan gum biopolymer as a sustainable solution for soil properties enhancement. The Atterberg limits, unconfined compression, CU and UU triaxial tests were performed to examine the effect of xanthan gum on the soil strength and plasticity. Additionally, the durability of biopolymer-treated and untreated soils under wetting and drying cycles and moisture susceptibility were assessed. The results showed that the compressive strength of soil increased by increasing the xanthan gum concentration and curing time and reached its peak value after a specific curing time. The addition of xanthan gum resulted in significant improvement in soil cohesion and caused a reduction in the internal friction angle of the soil. While increasing the number of wetting/drying cycles decreased the soil strength, the biopolymer-treated soil exhibited higher soil strength than the untreated soil. This study provides valuable experiences in the use of xanthan gum biopolymer in practical engineering applications.

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed.

This paper examines the validity of the plasticity index (PI) as a criterion for estimating the liquefaction potential of clayey soils under cyclic loading. The results of undrained cyclic stress-controlled ring-shear tests on artificial mixtures of sand with different clays saturated with water indicated that an increase in PI decreased the soil potential to liquefy, and soil with PI>15 seemed to be nonliquefiable, a finding that is in agreement with the results of other researchers. However, in this study some deviations from this relation were found when a bentonite–sand mixture was treated with solutions of different ions, thus bringing into question the effectiveness of PI as a measure of the liquefaction potential of clayey soil having a certain pore water chemistry.