EFFICIENT IOT-ENABLED HIGH SPEED AND ENERGY EFFICIENT EARLY LANDSLIDE DETECTION AND MONITORING SYSTEM BASED ON GEOTECHNICAL PARAMETERS

Landslides are a growing threat in steep regions of the world, taking lives and damaging property. The recent damages caused by landslides demand that authorities pay attention to catastrophe risk mitigation strategies. One crucial risk reduction strategy is the creation of an efficient landslide early warning system (LEWS), which will allow authorities and the public at large to be informed in advance of any landslide incidents. In order to construct a system of early warning for landslides, a wireless sensing network may collect data on the geological features and a few physical surroundings characteristics. The recommended system's primary objective is to predict when a landslide could happen and alert authorities to prevent or at least minimize casualties. In this study, we show how Internet of Things-based sensors (temperature, soil moisture ,humidity) may be used to observe and alert authorities to impending landslide danger. An advanced landslide tracking system built on Internet of Things infrastructure is shown here. The system is comprised of a group of self-sufficient wearable sensors, each of which wears a sensor costume designed for tracking landslides, and a microprocessor that aggregates data from a wide variety of sensors

Evaluation of engineering properties of clayey sand bio-mediated with terrazyme enzyme

Soil stabilization is a practical approach for enhancing the suitability of problematic soil in construction projects. This study focusses on analyzing the impact of the bio-enzyme Terrazyme on the engineering properties of Mirpur soil, which exhibits inadequate performance as subgrade soil, particularly in moist conditions. The study investigates key engineering characteristics, including unconfined compressive strength (UCS), California Bearing Ratio (CBR), maximum dry density (MDD), Atterberg’s Limits, and compressibility index. Additionally, X-Ray Diffraction and SEM analysis were conducted to identify the mineral composition and particle structure of Mirpur soil. It is demonstrated that the incorporation of Terrazyme enhanced the engineering properties of the soil. The findings will contribute to a better understanding of the efficacy of bio-mediated soil stabilization techniques

Guidelines for designing the overhead transmission tower foundations adjacent to cohesionless slopes

Foundations supporting electrical transmission line towers are subjected to oblique loading due to tower own weight along with wind loading. Due to land limitation and linear alignment of transmission lines, many power transmission towers are situated in sand near sloping grounds and proper evaluation of their foundation performance is essential for the safety and reliability of the critical power transmission lines. There is currently no guidance available on the behaviour of transmission towers foundations constructed near slopes. Thus, the primary objectives of this study are to evaluate the response of transmission tower foundations in cohesionless sloping grounds and investigate the effects of various parameters on their ultimate capacity. The research methodology consisted of three main components: i) conducting a series of laboratory tests on two types of overhead transmission tower foundations, namely: pad and inclined chimney footing and vertical and batter pier foundations, situated in sand near sloped grounds to evaluate their response under combined loads and comprehensive soil characterization so as to attain experimental results and necessary soil parameters for numerical modeling; ii) developing, calibrating, and validating nonlinear three dimensional (3D) finite element models (FEMs) employing the results of laboratory tests; and iii) performing a comprehensive parametric analysis on the response of both foundations under oblique loads considering a range of ground slope configurations and soil properties using the validated FE models. The investigated parameters include: slope height and inclination, foundation setback distance from the slope edge, load inclination, pier batter inclination, as well as soil strength and relative density. The results of laboratory tests and numerical simulations were analyzed to establish design guidelines for transmission line foundations in sloping grounds. Seven laboratory tests were carried out on pad and inclined chimney footings: three tests under 2 oblique compression loads in sloping grounds, one test under 2 oblique compression loads in a flat ground, and three tests under 2 oblique pullout loads in sloping grounds. In addition, thirteen laboratory tests were conducted on pier foundations: one test under axial compression loads in a flat ground, one test under pure lateral loads in a flat ground, one test under 1 oblique compression loads in a flat ground, five tests under 1 oblique compression loads in sloping grounds, one test under 1 oblique pullout loads in a flat ground, and four tests under 1 oblique pullout loads in sloping grounds. For pad and inclined chimney footings, both experimental and numerical results revealed that an increase in the slope height and slope inclination and a decrease in the setback distance of the footing from the slope edge result in significant decrease in the footing capacity. It was also found that, as expected, load inclination and lower soil relative density resulted in further reduction of footing ultimate capacity. Finally, the results demonstrated that a setback distance of 2 to 5B eliminated the effect of the slope on the footing ultimate bearing capacity. The experimental and numerical results of pier foundations revealed that the peak soil pressure and the rotation point occur at depths of and , respectively, below the ground surface in case of pure lateral loading. It was also found that, in case of pure vertical loading, less than 30% of the applied load was transmitted to the soil by the pier shaft resistance, indicating that the pier toe resistance had a significant contribution. The results also indicated that the ultimate capacity of an obliquely loaded pier buried in a flat ground was greater by 17% than that of a vertically loaded pier in a flat ground. Furthermore, the results indicated that the load carrying capacity of vertical and battered piers increase significantly as and increased and decreased. The ultimate capacity of vertical pier () increased as increased due to the significant increase in shaft resistance. For example, the shaft resistance increased from 15% to 30% of pile capacity as increased from 5° to 25°. In addition, the ultimate capacity of battered pier () increased as θ decreased. Finally, the effect of slope on the capacity of both vertical and batter piers vanished at a setback distance of 3 to 10 times the pier diameter, depending on the slope, load, and batter inclinations as well as the soil relative density . This means that the pier capacity reaches that of a pier buried in a flat ground beyond these distances

Tribological properties of CNT-filled epoxy-carbon fabric composites: Optimization and modelling by machine learning

Polymer matrix composites reinforced with fibers/fillers are extensively used in several tribological components of automotive and boating applications. The mechanical performance of polymer composites improves by incorporating nanofillers as secondary reinforcement. The present research work fabricated carbon fabric-reinforced epoxy composites using the hand layup. The carbon fabric-reinforced polymer composites were fabricated with 0.1 wt%, 0.2 wt%, and 0.5 wt% of carbon nanotubes (CNT) fillers as secondary reinforcement. Tribological properties of carbon fabric-reinforced epoxy composites filled with CNT have been carried out using a pin‐on‐disc method. Adding fillers significantly improves the tribological behaviour of the carbon fabric-reinforced epoxy composites by reducing wear rate and coefficient of friction. The large surface area of interaction due to the higher aspect ratio of CNT shows improved adhesion between epoxy matrix and carbon fabrics. It improves the various mechanical and tribological characteristics of composites—also, an analysis of worn surfaces is carried out to analyze the wear mechanisms using scanning electronic microscopy. The research employs a combination of experimental analyses and machine learning (ML) techniques to explore the wear resistance, hardness, and predictive modeling of volume loss in the composites. The hyperparameter fine-tuning of ML algorithms, including Random Forest (RF), k-Nearest Neighbors (KNN), and XGBoost, demonstrates superior predictive capabilities, particularly with RF. The study bridges material science, ML, and practical applications, contributing valuable insights for developing advanced composite materials