Calcium-based binders, such as ordinary Portland cement (OPC) and lime (CaO), are the most common artificial cementitious materials used worldwide for concrete and soil improvement. However, using cement and lime has become one of the main concerns for engineers because they negatively affect the environment and economy, prompting research into alternative materials. The energy consumption involved in producing cementitious materials is high, and the subsequent CO₂ emissions account for 8% of the total CO₂ emissions. In recent years, an investigation into cement concrete’s sustainable and low-carbon characteristics has become the industry’s focus, achieved by using supplementary cementitious materials. This paper aims to review the problems and challenges encountered when using cement and lime. Calcined clay (natural pozzolana) has been used as a possible supplement or partial substitute to produce low-carbon cement or lime from 2012–2022. These materials can improve the concrete mixture’s performance, durability, and sustainability. Calcined clay has been utilized widely in concrete mixtures because it produces a low-carbon cement-based material. Owing to the large amount of calcined clay used, the clinker content of cement can be lowered by as much as 50% compared with traditional OPC. It helps conserve the limestone resources used in cement manufacture and helps reduce the carbon footprint associated with the cement industry. Its application is gradually growing in places such as Latin America and South Asia.

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

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.

Waste products have recently been used as one of the techniques in soil stabilization. The material is not just environmentally friendly, but also cheap. In this study, two different types of soil stabilizer—lime and alkaline activator (AA) with the inclusion of treated coir fibre as soil reinforcement in marine clay soil—were examined. The inclusion of fibre in the treated soil has had a positive impact in increasing the strength of the soil. Therefore, to assess the effectiveness of the soil treatment, mechanical tests such as indirect tensile strength, flexural test and unconfined compressive strength test were performed at three different curing periods (7, 28 and 90 days) on both untreated and treated soil. From the results, the inclusion of fibre in both lime and alkaline activation indicates an enhancement on post-peak behaviour from brittle to more ductile. Microstructural analyses of Field Emission Scanning Electron Microscope (FESEM) and Energy Dispersive X-ray (EDX) were also conducted after shearing to evaluate the changes of the soil before and after the treatment. Overall, results indicate that the treatment transformed the structure of the soil to become denser where it filled the large pores compared to untreated soil.

The durability of natural and treated clay soil stabilized with lime and alkaline activation (AA) affected by environmental factors (hot and humid) was determined in this study. Investigation and evaluation on the strength of the soil, moisture content, and volume change of the specimen were determined at each curing period (7, 28, and 90 days) based on the weather conditions. An unconfined compressive strength (UCS) of the specimen at three different wetting/drying cycles (one, three, and five cycles) was determined. The findings show that the strength of the treated specimens fluctuated with increment and decrement strength (one and three cycles) in the range of 1.41 to 1.88 MPa (lime) and 2.64 to 8.29 MPa (AA), while for five cycles with a curing period of 90 days the decrement was in the range of 1.62 to 1.25 MPa and 6.06 to 5.89 MPa for lime and AA, respectively. The decrement percentage for treated samples that were subjected to five cycles of wetting and drying in 90 days was found to be 20.38% (lime) and 38.64% (AA), respectively. Therefore, it can be summarized that wetting/drying cycles have a significant influence on the durability, strength, and the volume changes of the specimens.

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.