The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R²), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R² value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models’ lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials.

The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.