Evaluating the suitability of incorporating sugarcane bagasse ash, polypropylene fibers, and sea sand-seawater in enhancing physico-mechanical properties of lightweight foamed concrete.

This study aims to explore the feasibility of replacing traditional components, such as Portland cement, river sand and tap water with sugarcane bagasse ash (SCBA), polypropylene (PP) fibers, and sea sand-seawater (SSSW) in lightweight foamed concrete (LWFC) production. SCBA was used in the range from 0 to 15% as cement replacement, and PP fibers were used with dosage from 0% to 1% by volume of LWFC. Meanwhile, SSSW was used to completely replace river sand and tap water.

Evaluation and estimation of compressive strength of concrete masonry prism using gradient boosting algorithm.

The compressive strength (CS) of the hollow concrete masonry prism is known as an important parameter for designing masonry structures. In general, the CS is determined using laboratory tests, however, laboratory tests are time-consuming and high-cost. Thus, it is necessary to evaluate and estimate the CS using different methods, for example, machine learning techniques.

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods.

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area.

Difference in Strength Development between Cement-Treated Sand and Mortar with Various Cement Types and Curing Temperatures.

To improve the strength of cement-treated sand effectively, the use of various cement types was investigated at different curing temperatures and compared with the results obtained from similar mortars at higher cement contents. The compressive strengths of cement-treated sand specimens that contained high early-strength Portland cement (HPC) cured at elevated and normal temperatures were found to be higher than those of specimens that contained ordinary Portland cement (OPC) and moderate heat Portland cement at both early and later ages. At 3 days, the compressive strength of the HPC-treated sand specimen, normalized with respect to that of the OPC under normal conditions, is nearly twice the corresponding value for the HPC mortar specimens with water-to-cement ratio of 50%.

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis.

In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed.