Smart Buildings
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Editors
Dr. Carlos Jimenez-Bescos
Dr. Carlos Jimenez-Bescos
E-Mail
Website1
Website2
Collection Editor
Department of Civil Engineering and Energy Technology, OsloMet—Oslo Metropolitan University, Pilestredet 35, PB 4, Saint Olavs Plass, 0130 Oslo, Norway
Interests: passivhaus; energy monitoring; IAQ; sustainable construction; building simulation
Dr. Habtamu Bayera Madessa
Dr. Habtamu Bayera Madessa
E-Mail
Website1
Website2
Collection Editor
Department of Civil Engineering and Energy Technology, OsloMet—Oslo Metropolitan University, Pilestredet 35, PB 4, Saint Olavs Plass, 0130 Oslo, Norway
Interests: building energy and indoor environment performance; energy system; solar energy; PCM; building physics; optimization
Topical Collection Information
Dear Colleagues,
According to an International Energy Agency (IEA) report, all new buildings should be zero-carbon ready by 2030, and 50% of the existing buildings should be retrofitted to a zero-carbon level by 2040 to reach the net-zero emission (NZE) strategy by 2050. Achieving high energy efficiency for new constructions, significantly reducing the energy usage of existing old buildings, and ensuring the increasing usage of on-site renewable energy became the primary goals for building sectors. This essentially leads to “smart, sustainable and inclusive” growth towards an energy-efficient and low-carbon economy. The strategic goal of achieving smart buildings relies on smart design, based on smart shapes, smart envelopes, smart systems, and smart materials, including smart management and smart user behaviors. The building quality objectives in terms of functionality, indoor well-being, efficiency and environmental impacts, and competitiveness could be achieved by smart buildings throughout their entire life cycle.
This topical collection solicits novel works in the domain of smart buildings. The following topics are proposed for this collection (but it is not limited to them):
- Energy-efficient retrofit solutions for buildings.
- Advanced building materials.
- Digitalization and smart controls in buildings.
- Building data analytics.
- Nearly zero-energy buildings and zero-emission buildings.
- District heating networks.
- Heat pumping technology.
- Green infrastructures.
- LCA and carbon assessments.
- Environmental assessment methods.
- Distributed solar PV.
- Indoor air quality management.
- Sustainable built environment.
Dr. Arnab Chaudhuri
Dr. Carlos Jimenez-Bescos
Dr. Habtamu Bayera Madessa
Collection Editors
Manuscript Submission Information
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Keywords
- healthy buildings
- retrofit solutions
- energy efficiency
- embodied carbon
- renewable energy
- smart materials
- indoor air quality
- thermal comfort
- built environment
- model-based predictive control
- sustainability
Published Papers (2 papers)
Open AccessArticle
Service Life Prediction and Life Cycle Costs of Light Weight Partitions
by
Alon Urlainis, Monica Paciuk and Igal M. Shohet
Viewed by 423
Abstract
This study investigates the life expectancy (LE) and life cycle costs (LCC) of three alternatives of interior partitions in residential units: gypsum board, autoclaved concrete block, and hollow concrete block partitions. The aim is to examine the sustainability and cost-effectiveness of these partitions
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This study investigates the life expectancy (LE) and life cycle costs (LCC) of three alternatives of interior partitions in residential units: gypsum board, autoclaved concrete block, and hollow concrete block partitions. The aim is to examine the sustainability and cost-effectiveness of these partitions in various service and occupancy conditions. Three different service conditions were analyzed: Standard (constructed without faults), Inherent Defect Conditions (with initial, non-progressing defects), and Failure Conditions (developing defects over time). To analyze the impact of occupancy conditions, six ‘negative occupancy factors’ were identified that accelerate partition deterioration, including non-ownership, poor maintenance, high residential density, the presence of young children, the presence of domestic animals, and the density of furniture. These factors define four occupancy condition categories: light, moderate, standard, and intensive. The research found that hollow concrete block partitions are the most durable, exceeding 100 years in light or moderate conditions. Gypsum board partitions, while cost-effective, have a lower life expectancy, needing replacement in 11–27 years in intensive conditions. Autoclaved concrete blocks offer moderate durability, with similar costs to hollow blocks in normal conditions. Overall, the study highlights the influence of service and occupancy on the lifespan of interior building components, and provides recommendations for partition type selection that are based on specific conditions. These recommendations are a pivotal outcome, highlighting the study’s significant contribution to the understanding of the long-term performance and sustainability of building materials in residential construction.
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Open AccessArticle
Research on the Intelligent Auxiliary Design of Subway Station Building Space Based on Deep Learning
by
Jiang An, Jiuhong Zhang and Mingxiao Ma
Viewed by 954
Abstract
In recent years, deep learning methods have been used with increasing frequency to solve architectural design problems. This paper aims to study the spatial functional layout of deep learning-assisted generation subway stations. Using the PointNet++ model, the subway station point cloud data are
[...] Read more.
In recent years, deep learning methods have been used with increasing frequency to solve architectural design problems. This paper aims to study the spatial functional layout of deep learning-assisted generation subway stations. Using the PointNet++ model, the subway station point cloud data are trained and then collected and processed by the author. After training and verification, the following conclusions are obtained: (1) the feasibility of spatial deep learning for construction based on PointNet++ in the form of point cloud data is verified; (2) the effectiveness of PointNet++ for the semantic segmentation and prediction of metro station point cloud information is verified; and (3) the results show that the overall 9:1 training prediction data have 60% + MIOU and 75% + accuracy for 9:1 training prediction data in the space of 20 × 20 × 20 and a block_size of 10.0. This paper combines the deep learning of 3D point cloud data with architectural design, breaking through the original status quo of two-dimensional images as research objects. From the dataset level, the limitation that research objects such as 2D images cannot accurately describe 3D space is avoided, and more intuitive and diverse design aids are provided for architects.
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