An analysis of LULC changes for understanding the impact of anthropogenic activities on food security: a case study of Dudhganga watershed, India.

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes.

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms.

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches.

Shear Strength of Nano Silica High-Strength Reinforced Concrete Beams.

In this study, the shear strength of sixteen full-scale over-reinforced concrete beams with and without nano silica (NS), constructed from high-strength concrete (HSC), was investigated both experimentally and analytically. Nano silica was used as a partial replacement for Portland cement. According to the NS ratio, the tested beams were divided into four groups: 0%, 1%, 2%, and 3%.

A Study for Improving Compressive Strength of Cementitious Mortar Utilizing Magnetic Water.

This research investigates the means to improve the compressive strength of mortar mixtures through using novel mixtures. These mixtures include magnetic water (MW) and fly ash (FA). MW was obtained by circulating tap water (TW) through a magnetic field.

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.

The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors.