Experimental Study on Performance of Reinforced Concrete (RC) Beams with Varied Proportions of Fly Ash
Assessment of Meteorological Droughts in Three Regions of Bengaluru, Karnataka
Comparative Analysis and Design of Integral Bridges: Thermal Effects, Soil-Structure Interaction, and Structural Performance
Evaluation of Bamboo Leaf Ash as Supplementary Cementitious Material in Concrete
Deep Learning - Based Detection of Fine Cracks in High-Resolution Concrete Dam Surfaces
Study on Strength Properties of Lightweight Expanded Clay Aggregate Concrete
A Step By Step Illustrative Procedure to Perform Isogeometric Analysis and Find the Nodal Displacements for a Two Dimensional Plate Structure
Lateral - Torsional Buckling of Various Steel Trusses
Comparative Study on Methodology of Neo-Deterministic Seismic Hazard Analysis Over DSHA and PSHA
A Step by Step Procedure to Perform Isogeometric Analysis of Beam and Bar Problems in Civil Engineering Including Sizing Optimisation of a Beam
Investigation on the Properties of Non Conventional Bricks
Analysis on Strength and Fly Ash Effect of Roller Compacted Concrete Pavement using M-Sand
Investigation on Pozzolanic Effect of Mineral Admixtures in Roller Compacted Concrete Pavement
Effect of Symmetrical Floor Plan Shapes with Re-Entrant Corners on Seismic Behavior of RC Buildings
Effect of Relative Stiffness of Beam and Column on the Shear Lag Phenomenon in Tubular Buildings
This study investigates the performance of reinforced concrete (RC) beams incorporating different proportions of fly ash (FA). A total of five beams, each with dimensions of 230 mm x 300 mm x 1600 mm, were constructed. Four beams were grade M20 (with a 28-day compressive strength of 20 MPa), with FA content varying at 0%, 5%, 20%, and 30%, while one beam was grade M25 with 20% FA. The M20 grade beam without FA served as the control beam (CB). After a 28-day curing period, the beams were tested using a three-point bending system, with parameters including central displacement, failure load, failure mode, and crack patterns. The performance of the beams was compared to that of the control beam. The findings indicated that the beam with 20% FA showed similar load-displacement behavior to the control beam, suggesting that 20% FA is the optimal proportion for enhanced performance.
Drought is a common, natural, and recurrent climatic phenomenon that can occur in any climatic region, causing water shortage. This can be analyzed by applying the Standardized Precipitation Index (SPI), departure analysis of annual rainfall from long-term average, and probability distribution analysis of annual rainfall. The SPI can characterize droughts by the likelihood of occurrence of wet and dry events on seasonal time scales and their severity. Rainfall departure and probability distribution analysis are found to be simple techniques to assess the drought frequency on the regional scale. This study focused on evaluating the drought characteristics in the Kengeri, Tavarekere, and Uttarahalli meteorological regions of Bengaluru. For this purpose, seasonal (viz., pre-monsoon, monsoon, and post-monsoon) and annual rainfall series were derived from the daily rainfall data observed at three meteorological stations during the period 1980 to 2022 and used in drought analysis. The SPI analysis indicated that the probability of occurrence of extreme wet years in pre- monsoon and post-monsoon periods of Kengeri are about 13% and 30%, respectively, whereas these values are computed as about 20% in pre-monsoon and 35% in post-monsoon for Tavarekere. For Uttarahalli, based on SPI values, the probability of occurrence of extreme wet years in pre-monsoon and post-monsoon periods is about 14% and 51%, respectively. The annual rainfall departure analysis indicated that the number of severe drought years is two each for Kengeri and Tavarekere, whereas the number of severe drought years for Uttarahalli is found to be seven. The probability distribution analysis of annual rainfall indicated that Kengeri, Tavarekere, and Uttarahalli are drought-prone regions. The results presented in the study are the indicators to assess the severity and determine the extent of meteorological drought while planning water resources and drought mitigation in the study area.
The integral bridges have emerged as a preferred alternative to traditional bridges with expansion joints, particularly for small- to medium-span structures. Integral bridges eliminate the need for expansion joints, leading to reduced maintenance and improved long-term performance. This study presents a comprehensive analysis of integral bridges, focusing on the validation of P-Y curves for modeling soil-pile interaction, the impact of thermal loading on pile behavior, and the comparison of structural responses between integral and simply supported bridges. Using the nonlinear analysis tool SAP2000, the research evaluates key factors such as pile deflection, bending moments, shear forces, and deck slab stresses under various loading conditions, including IRC Class A and 70R. The results show that integral bridges exhibit lower deck slab stresses and bending moments compared to simply supported bridges, highlighting their efficiency in specific design scenarios. The study concludes with recommendations for enhancing the design and construction of integral bridges, especially in the absence of specific code provisions.
The application of materials exhibiting pozzolanic properties in concrete has become the need of the hour. Evaluating the pozzolanic activity of materials that replace cement is gaining significance due to the growing demand for environmentally sustainable cement products. Incorporating bamboo leaf ash (BLA) as supplementary cementitious material (SCM) in concrete aligns with the principles of sustainable construction by reducing waste, utilizing renewable resources, and potentially lowering carbon emissions associated with traditional cement production. In this study, BLA is used as a partial replacement for ordinary Portland cement (OPC) in ranges of 10%, 20%, and 30%. Experimental results on hardened properties showed that concrete mixed with BLA gives satisfactory cube compressive strength up to the replacement level of 20% with OPC as a binder. Beyond 20% replacement, cube compressive strength falls below the characteristic strength of concrete.
An innovative deep learning-based method is introduced for detecting fine cracks in high-resolution images of concrete dam surfaces, addressing the urgent need for efficient and accurate maintenance of these vital infrastructures. Traditional manual inspection methods often fail to detect subtle cracks, leading to potential safety hazards and costly repairs. By leveraging advanced deep learning techniques, this study develops a model that automates the detection process, improving both precision and efficiency. High-resolution images are collected and meticulously annotated to create a comprehensive dataset, which is then used to train a deep learning model designed for crack identification. The model's performance is evaluated against a separate test dataset, demonstrating significant improvements in detection accuracy. This automated approach not only facilitates timely interventions but also contributes to enhanced monitoring strategies, ultimately ensuring the structural integrity and safety of concrete dams in the long term.
