India’s fossil-fuel-based energy dependency is up to 68%, with the commercial and residential sectors contributing to the rise of building energy demand, energy use, and greenhouse gas emissions. Several studies have shown that the increasing building energy demand is associated with increased space-cooling ownership and building footprint. The energy demand is predicted to grow further with the conditions of global warming and the phenomenon of urban heat islands. Building designers have been using state-of-the-art transient simulation tools to evaluate energy-efficient envelopes with present-day weather files that are generated with historical weather datasets for any specific location. Designing buildings with historical climatic conditions makes the buildings vulnerable to the predicted climate change impacts. In this paper, a weather file generator was developed to generate Indian future weather files using a geo-filtering-based spatial technique, as well as the temporal downscaling and machine learning (ML)-based bias correction approach proposed by Belcher et al. The future weather files of the three representative concentration pathways of 2.6, 4.5, and 8.5 could be generated for the years 2030, 2050, 2070, 2090, and 2100. Currently, the outputs of the second-generation Canadian Earth System Model are being used to create future weather files that will aid architects, urban designers, and planners in developing a built environment that is resilient to climate change. The novelty lies in using observed historical data from present-day weather files on the typical meteorological year for testing and training ML models. The typical meteorological weather files are composed of the concatenation of the monthly weather datasets from different years, which are referred to for testing and training ML models for bias correction.
Reinforced cement concrete (RCC) is universally acknowledged as a low-cost, rigid, and high-strength construction material. Major structures like buildings, bridges, dams, etc., are made of RCC and subjected to repetitive loading during their service life for which structural performance deteriorates with time. Bridges and high-rise structures, being above ground level, are hard to equip with the contact mechanical methods to inspect strains and displacements for structural health monitoring (SHM). A non-contact, optical and computer vision based full field measuring technique called digital image correlation (DIC) technique was developed in the recent past to specifically evaluate bridge decks. Generally, optical images of structure in field conditions are not acquired precisely perpendicular to the object, which instinctively affects the deformation results obtained during loading conditions. An unmanned aerial vehicle (UAV) equipped with DIC vision-based technique acts as a rapid and cost-effective tool to quantify the serviceability of bridges by measuring strains and displacements at inaccessible locations. In this study, a non-contact unmanned aerial vehicle image correlation (UAVIC) technique is used on a scaled bridge girder and a contact method of measuring deformations with a dial gauge. Both investigations are correlated for accuracy assessment, and it is understood that results in laboratory conditions are 90% accurate. Similarly, the UAVIC technique is also performed on a rail over the bridge in the field conditions to understand the feasibility of the proposed method and evaluate damage quantification of it.
Developing countries such as Iran are rapidly expanding, putting pressure on non-renewable energy resources. The building sector takes a major share of the total energy consumption of the country and is projected to increase further, resulting in the call for strategies to reduce energy use by improving the thermal performance of buildings. This study addresses the compelling need to provide optimum design guidelines for future apartment buildings in the city of Shiraz by investigating two urban cluster typologies, stair and pyramid, arranged in five orientations. The results showcase the ideal combination of 155° for the Pyramid typology, which contributes the least to the annual energy loads of the buildings.
Most of the southern Indian cities are in the warm and humid coastal belt where the summer and winter mean temperature varies between 25 and 35 °C and 20 and 30 °C, respectively, with mean relative humidity, responsible for mould growth in buildings, ranging as high as 70–90% across the year. This paper focuses on identifying the mould growth index (MGI) using Heat and Mass Transfer analysis in EnergyPlus (v-9.3) for an autoclaved aerated concrete (AAC) wall assembly in the warm–humid climate of Mangalore. It is found that AAC has an annual mean MGI of 3.5, and that key drivers for mould growth are surface temperature and surface humidity.
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