Faculty Publications
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Item Thermal and cost analysis of float and various tinted double window glass configurations on heat gain into buildings of hot & dry climatic zone in India(International Information and Engineering Technology Association info@iieta.org, 2018) Gorantla, G.; Saboor, S.; Setty, A.B.T.P.R.Glass window enclosures for buildings consume a lot of energy for affording thermal and visual comfort. Reducing solar radiation in summer and increase in winter through different double window glasses for making energy efficient building design is the theme of this paper. Therefore this work measures the spectral characteristics of four glasses namely grey, green, bronze and clear glasses in entire solar spectrum region from 300nm to 2500nm at normal angle of incidence by using Shimadzu UV 3600 spectrophotometer based on ASTM standards. To find the solar optical properties a MATLAB code was used which is based on British standards. To find the solar radiation transmission from different double window glass configurations and cost analysis from eight coordinal directions at peak summer and winter day were selected as per Indian standards with a MATLAB code to hot and dry climatic zone of Jodhpur (26.300N, 73.020E). From these results it is shows that in south direction all double glass windows are gaining less heat in summer and more heat in winter season when compared to other orientations. It is found that south orientation C1(Greyglasswindow-Airgap10mm-Greenglasswindow) and C12(Clearglasswindow-Airgap10mm-Bronzeglasswindow) configuration windows are gaining minimum and maximum heat in summer and winter respectively when compared to other configuration windows. Among all windows C1 configuration window is saving more cost annually. © 2018 International Information and Engineering Technology Association. All rights reserved.Item Prediction of thermal conductivity of quartz chlorite schist rocks: a comparative study of MLR and ridge regression(Inderscience Publishers, 2025) Tripathi, A.K.; Pal, S.K.; Dileep, G.; Raj, A.Thermal conductivity is a key physical property with broad applications in engineering and geosciences, particularly in energy-efficient building design, geothermal energy systems, and subsurface geological studies. Accurate determination of thermal conductivity is essential for understanding heat transfer mechanisms in rock materials. However, direct in-situ measurement is often impractical due to technical and logistical constraints. As a result, indirect estimation methods, which establish empirical relationships between thermal conductivity and various physico-mechanical properties, have gained attention. This study investigates the thermal conductivity of quartz chlorite schist through laboratory experiments, alongside measuring key physico-mechanical properties, including P-wave velocity, porosity, density, and uniaxial compressive strength (UCS). The objective is to analyse the correlations between thermal conductivity and these properties to develop a reliable predictive model. Multiple regression and ridge regression analysis are employed to derive an empirical equation for estimating thermal conductivity based on the measured parameters. The findings of this study contribute to improving indirect assessment techniques, which are valuable for geotechnical and geological applications where direct measurements are challenging. © © 2025 Inderscience Enterprises Ltd.Item Predicting joint shear in beam–column connections using convolutional neural networks(Springer Science and Business Media B.V., 2025) Sidvilasini, S.; Palanisamy, T.Predicting joint shear at beam-column junctions (BCJ) is essential in structural engineering to ensure the safety and reliability of systems. Current methodologies using empirical calculations may rely on simplistic assumptions and insufficiently account for the many geometric factors and material properties that influence shear in BCJ. This research introduces a novel approach using Convolutional neural networks (CNNs) to predict joint shear. The collection comprises 515 joints, categorized into 210 exterior joints and 305 interior joints, characterized by 14 fundamental factors delineating their form and material properties. The predictive performance of the CNN model has been evaluated using known engineering codes, including ACI 318-19, NZS 3101:1-2006, IS 13920:2016, and several other data-driven models in the domain. Furthermore, it has been contrasted with an ensemble regression method. The study includes a thorough sensitivity analysis using a gradient-based method to determine the relative importance of input factors in predicting shear stress. The findings demonstrate the effectiveness of CNN in identifying complex relationships among joint parameters, thereby enabling precise predictions of joint shear. This method offers a promising alternative to traditional empirical formulas and enhances the understanding of structural behavior in BCJ. This study integrates contemporary machine learning algorithms with structural engineering concepts to optimize design processes and augment the safety and reliability of built environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Item AN ONTOLOGY-DRIVEN BI-DIRECTIONAL WORKFLOW FOR INTEGRATING PROJECT MANAGEMENT DATA INTO THE IFC STANDARD(International Council for Research and Innovation in Building and Construction, 2025) Kone, V.; Mahesh, G.The evolution of Building Information Modelling (BIM) towards a data-centric paradigm is often hindered by challenges in semantic interoperability, particularly when integrating project management data with the Industry Foundation Classes (IFC) standard. While IFC enables syntactic data exchange, a persistent gap exists dynamically linking building geometry with the complex, relational information of project schedules, resources, and costs in a semantically consistent, interoperable manner. This paper presents a novel, bi-directional methodology that leverages Semantic Web technologies (RDF, OWL, SPARQL) to address this challenge. The core of the methodology is an ontology-driven workflow that uses two purpose-built ontologies: BIMOnto, a lightweight representation of the building asset derived from if cOWL, and IproK (Integrated Project Knowledge Ontology), which formally structures project management information across schedule, resource, and cost domains. The workflow enables both directions: (1) transforming IFC models into queryable knowledge graphs, and (2) programmatically generating new, enriched IFC models from unified knowledge graphs. This reverse transformation creates native, standards-compliant IFC entities for tasks (IfcTask), resources (IfcResource), costs (IfcCostItem), and their standard relationships (IfcRelAssignsToProduct, etc.), moving beyond custom property sets. The feasibility and effectiveness of this approach are validated through a case study using a multi-story residential building model, demonstrating the successful generation of a verifiable, integrated BIM artifact. The findings show that this ontology-driven framework significantly enhances data integration, creating truly interoperable models where process data becomes a first-class citizen within the BIM environment, advancing the potential for more intelligent, data-centric BIM practices throughout the project lifecycle. © © 2025 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.