A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry

Buildings, Journal Year: 2024, Volume and Issue: 14(4), P. 1113 - 1113

Published: April 16, 2024

Carbon emissions present a pressing challenge to the traditional construction industry, urging fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, artificial intelligence have enabled integrated digital technologies (e.g., twins) as promising trend achieve emission reduction net-zero. While twins sector shown rapid growth recent years, most applications focus on improvement of productivity, safety management. There is lack critical review discussion state-of-the-art improve sustainability this sector, particularly reducing carbon emissions. This paper reviews existing research where been directly used enhance throughout entire life cycle building (including design, construction, operation maintenance, renovation, demolition). Additionally, we introduce conceptual framework for which involves elements twin implementation process, discuss challenges faced during deployment, along with potential opportunities. A proof-of-concept example also presented demonstrate validity proposed enhanced sustainability. study aims inspire forward-thinking innovation fully exploit transform industry into sector.

Language: Английский

---

Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 61, P. 104867 - 104867

Published: July 27, 2024

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older contribute greatly to waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering bills, extending the life of these historic landmarks all depend on fixing inefficiency that plagues buildings. Despite advanced technologies' remarkable progress, potential Internet Things deep learning has not been unexplored. Major obstacles include expensive out-of-date infrastructure difficulty incorporating technology into historically significant structures. Existing research mostly ignored infrastructures' unique requirements limitations favour current or newly built services. In addition, comprehensive integrating with this specific environment lacking. Smart building management made possible by (IoT) learning. Architectural limitations, outmoded infrastructure, necessity non-invasive retrofitting solutions monitoring improvement This proposes combining IoT Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) make an system can change adapt needs Data from sensors collected occupancy, temperature, lighting, equipment usage then analyzed using Learning models determine most efficient consumption patterns. Beyond its energy-saving potential, method many uses. Spotting structural problems before become major improve occupant comfort, reduce maintenance costs, pave way predictive maintenance. Integration grid demand response programs be facilitated, too, improving reliability power as a whole. Our Learning-based solution optimizes usage, reduces expenses, mitigates impact buildings, shown extensive simulation studies. The system's performance compared more conventional methods, flexibility evaluated various contexts.The experimental outcomes show suggested DL-PEM model increases forecasting analysis, thermal comfort optimization seasonal variation occupancy data analysis

Language: Английский

---

A Systematic Review of the Applications of AI in a Sustainable Building’s Lifecycle

Buildings, Journal Year: 2024, Volume and Issue: 14(7), P. 2137 - 2137

Published: July 11, 2024

Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) enhance sustainability throughout a building’s lifecycle. The identifies AI technologies applicable sustainable building practices, examines their influence, analyses implementation challenges. findings reveal AI’s capabilities in optimising efficiency, enabling predictive maintenance, aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. also barriers adoption, including cost concerns, data security risks, While offers innovative solutions optimisation environmentally conscious addressing technical practical challenges is crucial its successful integration practices.

Language: Английский

---

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Sustainability, Journal Year: 2024, Volume and Issue: 16(9), P. 3627 - 3627

Published: April 26, 2024

Global warming, climate change and the energy crisis are trending topics around world, especially within sector. The rising cost of energy, greenhouse gas (GHG) emissions global temperatures stem from over-reliance on fossil fuel as major resource. These challenges have highlighted need for alternative resources urgent intervention strategies like consumption reduction improving efficiency. heating, ventilation, air-conditioning (HVAC) system in a building accounts about 70% consumption, decision to reduce may impact indoor environmental quality (IEQ) building. It is important adequately balance tradeoff between IEQ management. Artificial intelligence (AI)-based solutions being explored performance without compromising IEQ. This paper systematically reviews recent studies AI machine learning (ML) management by exploring common use areas, methods or algorithms applied results obtained. overall purpose this research add existing body work highlight energy-related applications buildings related gaps. result shows five application areas: thermal comfort air (IAQ) control; prediction; temperature anomaly detection; HVAC controls. Gaps involving policy, real-life scenario applications, insufficient study visual acoustic areas also identified. Very few take into consideration follow standards selection process positioning sensors buildings. reveals more summarized research.

Language: Английский

---

Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach

Applied Energy, Journal Year: 2024, Volume and Issue: 358, P. 122493 - 122493

Published: Jan. 9, 2024

We study the problem of tuning parameters a room temperature controller to minimize its energy consumption, subject constraint that daily cumulative thermal discomfort occupants is below given threshold. formulate it as an online constrained black-box optimization where, on each day, we observe some relevant environmental context and adaptively select parameters. In this paper, propose use data-driven Primal-Dual Contextual Bayesian Optimization (PDCBO) approach solve problem. simulation case single room, apply our algorithm tune Proportional Integral (PI) heating pre-heating time. Our results show PDCBO can save up 4.7% consumption compared other state-of-the-art optimization-based methods while keeping tolerable threshold average. Additionally, automatically track time-varying thresholds existing fail do so. then alternative where aim with budget. With formulation, reduces average by 63% safe required

Language: Английский

---

Sensitivity Analysis of Physical Regularization in Physics-informed Neural Networks (PINNs) of Building Thermal Modeling

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112693 - 112693

Published: Feb. 1, 2025

Language: Английский

---

A Meta-Survey on Intelligent Energy-Efficient Buildings

Big Data and Cognitive Computing, Journal Year: 2024, Volume and Issue: 8(8), P. 83 - 83

Published: July 30, 2024

The rise of the Internet Things (IoT) has enabled development smart cities, intelligent buildings, and advanced industrial ecosystems. When IoT is matched with machine learning (ML), advantages resulting enhanced environments can span, for example, from energy optimization to security improvement comfort enhancement. Together, ML technologies are widely used in particular, reduce consumption create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, models typically analyze predict various factors such as temperature, humidity, light, occupancy, human behavior aim optimizing building systems. literature, many review papers have been presented so far field IEEBs. Such mostly focus on specific subfields or a limited number papers. This paper presents systematic meta-survey, i.e., articles, that compares state art IEEBs using Prisma approach. more detail, our meta-survey aims give broader view, respect already published surveys, state-of-the-art IEEB field, investigating use supervised, unsupervised, semi-supervised, self-supervised variety IEEB-based scenarios. Moreover, compare surveys by answering five important research questions about definitions, architectures, methods/models used, datasets real implementations utilized, main challenges/research directions defined. provides insights useful both newcomers researchers who want learn methodologies IEEBs’ design implementation.

Language: Английский

---

Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures

Buildings, Journal Year: 2023, Volume and Issue: 13(12), P. 3018 - 3018

Published: Dec. 3, 2023

The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need devise an effective strategy for disasters. In event fire, it difficult evacuate in early stages loss detection function, difficulty securing visibility, confusion over evacuation routes. Accordingly, rapid rescue, necessary build city-level fire safety service digital system based on smart technology. addition, both forest building fires emit large amount carbon dioxide, which main cause global warming. Therefore, we prepare energy management achieve neutrality by 2030. this study, developed AI-based efficient integrated using city-based architecture. designed infrastructure buildings. proposal was demonstrated test bed A building, AR-based mobile/web application tested optimized management. Furthermore, optimal occupants were implemented through deep learning-based information data analysis. As result, paper presents four points management, demonstrate that optimization occupant ability saving can be achieved. We also analyze efficiency transfer rate prevent communication delays Virtual Edge Gateway (VEG) future, expect appearance future buildings research will produce more accurate prediction technology development cutting-edge city infrastructures.

Language: Английский

---

An Automatic Classification System for Environmental Sound in Smart Cities

Sensors, Journal Year: 2023, Volume and Issue: 23(15), P. 6823 - 6823

Published: July 31, 2023

With the continuous promotion of “smart cities” worldwide, approach to be used in combining smart cities with modern advanced technologies (Internet Things, cloud computing, artificial intelligence) has become a hot topic. However, due non-stationary nature environmental sound and interference urban noise, it is challenging fully extract features from model single input achieve ideal classification results, even deep learning methods. To improve recognition accuracy ESC (environmental classification), we propose dual-branch residual network (dual-resnet) based on feature fusion. Furthermore, terms data pre-processing, loop-padding method proposed patch shorter data, enabling obtain more useful information. At same time, order prevent occurrence overfitting, use time-frequency enhancement expand dataset. After uniform pre-processing all original audio, automatically extracts frequency domain log-Mel spectrogram log-spectrogram. Then, two different audio are fused make representation comprehensive. The experimental results show that compared other models, UrbanSound8k dataset been improved degrees.

Language: Английский

---

Foundational AI in Insurance and Real Estate: A Survey of Applications, Challenges, and Future Directions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 181282 - 181302

Published: Jan. 1, 2024

Language: Английский

---