Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies
Abstract
1. Introduction
- Analyze patterns and trends in the integration of AI and IoT technologies in cultural heritage preservation, focusing on risk management and environmental monitoring.
- Identify key factors and methodologies in applying AI and IoT to improve air quality, structural health monitoring, and conservation outcomes.
- Propose an innovative framework in heritage preservation that leverages advanced technologies for adaptive and proactive solutions to dynamic environmental challenges.
2. Materials and Methods
2.1. Database Search
2.2. Inclusion and Exclusion Criteria of the Study
2.3. Data Analysis and Synthesis Method
3. Results
3.1. Bibliometrix® Analysis
3.1.1. Research Gaps from Keywords Co-Occurrence Network
- Climate and Environmental Impact (green cluster): There is a significant grouping of terms related to climate effects, such as “temperature effect”, “climate models”, “airflow”, and “ventilation”. This highlights how environmental factors, such as changing climates and airflow, impact heritage buildings, indicating concern over physical deterioration and performance under varying conditions.
- Cultural Heritage and Risk Assessment (purple cluster): “Cultural heritage”, “building”, and “risk assessment” form another critical theme, connecting to concerns like “damage detection”, “decision-making”, and “microclimate”. These keywords suggest the importance of evaluating the risks that cultural heritage buildings face due to external factors, emphasizing preventive measures and monitoring.
- Air Quality and Pollution (red cluster): There is a prominent cluster related to air quality, including keywords such as “particulate matter”, “carbon dioxide”, and “atmospheric pollution”. This indicates a focus on how pollutants and environmental contaminants affect building materials, especially those used in heritage buildings like limestone and calcium carbonate.
- Historic Buildings and Conservation Strategies (blue cluster): Terms related to “preventive conservation”, “building materials”, and “monitoring” emphasize practical approaches to preserving heritage buildings through sustainable practices and careful material management.
- Architectural Design and AI (orange and brown clusters): Emerging technologies like “artificial intelligence” and “machine learning” are seen interacting with concepts related to “architectural heritage” and “building information modeling”, indicating that advanced computational methods are increasingly being applied to heritage preservation efforts.
3.1.2. Publications per Country
3.2. Environmental Risk Factors in Heritage Building Conservation
- Climatic factors: temperature, relative humidity, precipitation, wind, solar radiation, and atmospheric pressure directly affect structural integrity. Fluctuations cause expansion and contraction, leading to physical stress and degradation.
- Air quality factors: level of pollutants such as PM2.5, PM10, SO2, NO2, O3, and VOCs lead to soiling, corrosion, and chemical reactions that weaken materials. For example, SO2 and NO2 react with calcium carbonate-based materials, accelerating decay.
3.3. Technological Approaches to Risk Management in Heritage Buildings
3.3.1. Risk Types
- Structural Integrity Risks: IoT monitors real-time structural parameters like stress and strain, while AI and Heritage BIM (H-BIM) predict long-term vulnerabilities. Photogrammetry captures surface damage, and these technologies work together to ensure structural integrity.
- Microclimatic and Environmental Risks: IoT monitors temperature, humidity, and pollutants. Computational Fluid Dynamics (CFD) simulates environmental conditions, and AI predicts future impacts. This combination allows for continuous environmental risk assessments.
- Energy Efficiency Risks: IoT tracks energy consumption, while AI models suggest improvements and predict future energy needs. Dynamic energy models simulate performance, ensuring that energy efficiency is maintained.
- Decay and Surface Damage: Photogrammetry and Mask Region-based Convolutional Neural Network (Mask R-CNN) detect surface-level damage, while AI forecasts the progression of decay. This combination ensures that surface-level risks are addressed in a timely manner.
- Cultural and Historical Risks: H-BIM stores historical data about the building’s original design, materials, and modifications. AI analyzes the impact of proposed changes on cultural significance, ensuring that interventions preserve both physical and cultural heritage.
3.3.2. Data Sources
3.3.3. Frameworks
3.3.4. Methodologies
3.3.5. Technologies
- Artificial Intelligence (AI): AI technologies, such as ML algorithms (e.g., J48 decision trees and XGBoost) and DL models like Mask R-CNN, play an essential role in processing large datasets collected by IoT devices. AI-driven systems are capable of predicting structural decay, energy efficiency, and microclimatic changes. In heritage conservation, AI models also facilitate image-based analysis for detecting structural damage (e.g., cracks) using tools like Convolutional Generative Adversarial Networks (CGAN) and computer vision techniques [25,26,52,53,68,108]. In addition, crack detection and surface damage monitoring rely heavily on AI-powered models such as Fully Convolutional Networks (FCN), Mask R-CNN, and YOLO (You Only Look Once) object detection models. These models analyze image datasets from photogrammetry and laser scanning to identify and quantify cracks and surface damage, allowing for early intervention and repair. YOLOv8, the newest model in the YOLO algorithm series (YOLOv4 in [124] is an older version), offers advanced capabilities for real-time object detection, improving the accuracy of identifying structural damage. AI-powered tools like CNN algorithms, including the ResNet architecture, data labeling tools, and image processing software, significantly enhance the accuracy and efficiency of detecting and managing structural risks [52,59,108,121,125]. Python-based DL models further extend the AI framework, enabling customized algorithms for crack detection and damage assessment tailored specifically to the conditions and requirements of heritage sites. These models provide high flexibility and accuracy in image-based heritage monitoring [87,121].
- Internet of Things (IoT): IoT is a cornerstone technology for real-time environmental and structural monitoring in heritage buildings. IoT enables real-time monitoring through Wireless Sensor Networks (WSN), which track environmental conditions. The FIWARE platform provides a robust infrastructure for large-scale IoT deployments, enabling seamless communication between sensors and centralized monitoring systems. This platform integrates with Structural Health Monitoring (SHM) systems to gather and analyze real-time data on the structural condition of heritage sites. Various smart sensors are used to collect data, including NB-IoT-enabled smart sensors such as Sensirion SHTC3 (for temperature and humidity) and SAMYOUNG SY-DS-1L (for air quality). These sensors use Message Queue Telemetry Transport (MQTT) protocols to ensure low-power, long-range communication, making them ideal for hard-to-reach heritage sites. LoRa-enabled IoT edge devices and sensors like Bosch BME 280 (for temperature, humidity, and pressure) and CCS811 (for CO2 and VOCs) further extend the capabilities of the IoT network. Data collected by these sensors can be visualized and managed using platforms like Node-RED, ensuring that real-time insights are accessible to decision-makers [23,47,83,88,89,97]. In addition, Arduino-based sensor systems provide a customizable approach to collecting data on structural and environmental conditions. DHT11 (temperature/humidity), MPL3115A2 (pressure), and RD200M Rn (radon) sensors enhance the environmental monitoring scope, and FFT analysis (Fast Fourier Transform) is used to assess structural vibrations, ensuring that any early signs of damage are detected and addressed [59,108]. Moreover, risk assessment tools such as Art-Risk 3.5 are integrated into IoT frameworks to provide a comprehensive risk profile for heritage sites. By combining data from IoT sensors, SHM, and AI models, Art-Risk 3.5 enhances decision-making processes for preventive conservation and ensures that interventions are data-driven [26,59,103,108,119]. Radio Frequency Identification (RFID) technology is another valuable tool for material tracking in heritage conservation. It allows for the precise identification and monitoring of materials within heritage sites, contributing to better inventory management and ensuring that original or restoration materials are properly tracked and maintained [53].
- Heritage Building Information Modeling (H-BIM): H-BIM is an advanced digital platform that models heritage buildings’ structural and historical aspects. These models integrate data from various sources (IoT, AI, and sensor networks) and provide a holistic understanding of the building’s condition. By storing and analyzing data on the building’s materials, structure, and historical alterations, H-BIM helps predict and manage risks like structural decay and energy inefficiency. Integrating BIM Server and MongoDB ensures efficient data management, while Node-RED helps visualize the real-time data collected from IoT devices, making H-BIM a comprehensive tool for risk management [26,52,59,79,89,103]. Furthermore, Geometric Description Language (GDL) scripting is used within H-BIM to create precise parametric models of heritage buildings, capturing the intricate geometric details that are crucial for their preservation. GDL scripting allows the customization of digital models to reflect the unique structural and design elements of heritage sites, enhancing the accuracy of simulations and predictive analyses [59,125]. Revit plug-ins are also integral to H-BIM workflows, facilitating the integration of architectural models with real-time data collected from IoT devices and AI models. These plug-ins enable seamless interoperability between various digital tools used in heritage conservation, improving both workflow efficiency and model accuracy [52,108].
- Advanced Imaging Technologies: Technologies like photogrammetry, laser scanning, and drone-based photogrammetry are critical for high-resolution 3D modeling of heritage structures. Drones such as Anafi Parrot UAV are used for aerial photogrammetry, capturing detailed images that are processed into 3D models using Structure from Motion (SfM) algorithms and software like Context Capture. These models help detect surface damage and monitor structural changes over time. Photogrammetry is particularly valuable when combined with AI-based detection models (e.g., CNN algorithms for crack identification and surface damage detection). The Terrestrial Laser Scanner (TLS) is an essential tool in this domain, providing precise 3D measurements of heritage structures. Cyclone Register 360 is often used alongside TLS for accurate point cloud registration, ensuring that data from different scans is correctly aligned and processed into comprehensive 3D models [72,108,121,123,125].
- Computational Fluid Dynamics (CFD): CFD models simulate airflow, temperature, and humidity distribution, helping researchers understand how these factors influence the microclimate inside and around heritage buildings. Predictive simulations are crucial for assessing how heritage buildings respond to environmental factors over time. Climate impact assessments use models based on RCP2.6 and RCP8.5 scenarios to predict the effects of climate change on structural integrity and material preservation. Tools like Xfuzzy 3.0 are used to develop fuzzy logic algorithms that model uncertain environmental conditions, enabling predictive maintenance and risk management strategies. These algorithms are particularly effective when integrated with DTs, which simulate real-time responses of heritage buildings to environmental and structural stress [26,47,53,102,103].
- Digital Twin (DT): DT creates a dynamic virtual replica of a heritage building, integrating real-time data from IoT sensors with historical and structural information. DT enables continuous monitoring of the building’s condition and simulates how it responds to various factors such as environmental changes, structural stress, or maintenance actions. Acting as the convergence point for multiple technologies like IoT, AI, H-BIM, imaging, and predictive simulations, DT provides a comprehensive, real-time representation of the heritage building. By integrating data from these various technologies, DT creates a virtual environment that reflects both current and predicted conditions, offering insights for risk management and conservation strategies. This holistic approach allows heritage conservation teams to make informed decisions by not only monitoring present conditions but also forecasting future risks. DT transforms static building models into dynamic, responsive systems that evolve as the building and its surrounding environment change, ensuring the building’s long-term preservation and sustainability [26,52,59,79,89,93,103,108,125].
4. Discussion
4.1. Adoption Trends and Geographical Imbalance in AI and IoT Applications for Heritage Preservation
4.2. Comprehensive Environmental and Structural Monitoring Parameters
- Temperature and relative humidity: Fluctuations cause materials to expand and contract, leading to physical stress and degradation. Nearly all studies monitored these parameters, highlighting their fundamental importance.
- Air Pollutants: Pollutants like SO2, NO2, O3, CO2, PM2.5, PM10, and VOCs contribute to chemical reactions that weaken materials. Many studies focused on these pollutants.
- Microclimatic Factors: Some studies monitored parameters like air change rates, natural and artificial light, and microflora, reflecting an interest in comprehensive indoor environmental conditions.
4.3. Innovative Methodologies for Adaptive and Proactive Solutions
4.3.1. Integrated IoT and AI Systems for Real-Time Risk Management
4.3.2. Development of Comprehensive DTs
4.3.3. Enhanced Environmental Monitoring for Air Quality Improvement
4.3.4. Application of Advanced AI Techniques for Specific Conservation Challenges
4.3.5. Collaborative Platforms and Knowledge Sharing
4.4. Future Research Directions
4.4.1. Step 1: Preliminary Assessment
4.4.2. Step 2: Deployment of Monitoring Systems (Environmental and Structural Monitoring)
4.4.3. Step 3: Data Integration and Model Development (H-BIM Platform and AI Training)
4.4.4. Step 4: Development of the DT (Dynamic Virtual Representation)
4.4.5. Step 5: Model Validation and Accuracy Assessment
4.4.6. Step 6: Continuous Monitoring and Analysis
4.4.7. Step 6: Prevention Strategies and Maintenance Planning
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Nomenclature
ACH | Air Change Rates |
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
ASHRAE | American Society of Heating, Refrigerating and Air-Conditioning Engineers |
CFD | Computational Fluid Dynamics |
CGAN | Convolutional Generative Adversarial Networks |
CNN | Convolutional Neural Network |
DL | Deep Learning |
DRM | Disaster Risk Management |
DRR | Disaster Risk Reduction |
DT | Digital Twins |
FCN | Fully Convolutional Network |
FFT | Fast Fourier Transform |
GANs | Generative Adversarial Networks |
GDL | Geometric Description Language |
GIS | Geographic Information System |
H-BIM | Heritage Building Information Modeling |
HVAC | Heating, Ventilation and Air Conditioning systems |
IAQ | Indoor Air Quality |
IoT | Internet of Things |
Mask R-CNN | Mask Region-based Convolutional Neural Network |
ML | Machine Learning |
MQTT | Message Queue Telemetry Transport |
NB-IoT | Narrowband Internet of Things |
PM | Particulate Matter |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
RCP | Representative Concentration Pathways |
RFID | Radio Frequency Identification |
RH | Relative Humidity |
Rn | Radon (gas) |
SDGs | Sustainable Development Goals |
SfM | Structure from Motion |
SHM | Structural Health Monitoring |
SLR | Systematic Literature Review |
TLS | Terrestrial Laser Scanner |
UAV | Unmanned Aerial Vehicle |
UN | United Nations |
UV | Ultraviolet radiation |
VOCs | Volatile Organic Compounds |
WH | World Heritage |
WoS | Web of Science |
WSN | Wireless Sensor Networks |
XGBoost | eXtreme Gradient Boosting |
YOLO | You Only Look Once (object detection model) |
References
- Morkunaite, Z.; Podvezko, V.; Zavadskas, E.K.; Bausys, R. Contractor selection for renovation of cultural heritage buildings by PROMETHEE method. Arch. Civ. Mech. Eng. 2019, 19, 1056–1071. [Google Scholar] [CrossRef]
- Hollesen, J. Climate change and the loss of archaeological sites and landscapes: A global perspective. Antiquity 2022, 96, 1382–1395. [Google Scholar] [CrossRef]
- Bucur, E.; Vasile, A.; Diodiu, R.; Catrangiu, A.; Petrescu, M. Assessment of indoor air quality in a wooden church for preventive conservation. J. Environ. Prot. Ecol. 2015, 16, 7–17. [Google Scholar]
- Costa, M.; Carneiro, M.J. The influence of interpretation on learning about architectural heritage and on the perception of cultural significance. J. Tour. Cult. Change 2021, 19, 230–249. [Google Scholar] [CrossRef]
- Zhang, S.; Wang, B.y.; Wang, S.; Hu, W.; Wen, X.; Shao, P.; Fan, J. Influence of Air Pollution on Human Comfort in Five Typical Chinese Cities. Environ. Res. 2020, 195, 110318. [Google Scholar] [CrossRef]
- Faridi, S.; Yousefian, F.; Janjani, H.; Niazi, S.; Azimi, F.; Naddafi, K.; Hassanvand, M.S. The effect of COVID-19 pandemic on human mobility and ambient air quality around the world: A systematic review. Urban Clim. 2021, 38, 100888. [Google Scholar] [CrossRef]
- González-Lezcano, R.A. Editorial: Design of efficient and healthy buildings. Front. Built Environ. 2023, 9, 1210956. [Google Scholar] [CrossRef]
- Zoran, M.A.; Savastru, R.S.; Savastru, D.M.; Tautan, M.N. Impacts of exposure to air pollution, radon and climate drivers on the COVID-19 pandemic in Bucharest, Romania: A time series study. Environ. Res. 2022, 212, 113437. [Google Scholar] [CrossRef]
- UN. Hottest July Ever Signals ‘Era of Global Boiling Has Arrived’ Says UN Chief|UN News. Available online: https://news.un.org/en/story/2023/07/1139162 (accessed on 18 September 2024).
- Amnuaylojaroen, T. Perspective on the Era of Global Boiling: A Future beyond Global Warming. Adv. Meteorol. 2023, 2023, 5580606. [Google Scholar] [CrossRef]
- Abu-Allaban, M.; El-Khalili, M.M.M. Antiquity impact of air pollution at Gadara, Jordan. Mediterr. Archaeol. Archaeom. 2014, 14, 191–199. [Google Scholar]
- Mohd Dzulkifli, S.N.; Abdullah, A.H.; Leman, A.M. Design and material in museum: Does it affect the ventilation in indoor air quality? ARPN J. Eng. Appl. Sci. 2016, 11, 7341–7348. [Google Scholar]
- Oliveira, M.L.S.; Neckel, A.; Pinto, D.; Maculan, L.S.; Dotto, G.L.; Silva, L.F.O. The impact of air pollutants on the degradation of two historic buildings in Bordeaux, France. Urban Clim. 2021, 39, 100927. [Google Scholar] [CrossRef]
- Broomandi, P.; Jahanbakhshi, A.; Fathian, A.; Darynova, Z.; Janatian, N.; Nikfal, A.; Kim, J.R.; Karaca, F. Impacts of ambient air pollution on UNESCO world cultural heritage sites in Eastern Asia: Dose-response calculations for material corrosions. Urban Clim. 2022, 46, 101275. [Google Scholar] [CrossRef]
- Baglioni, M.; Poggi, G.; Chelazzi, D.; Baglioni, P. Advanced Materials in Cultural Heritage Conservation. Molecules 2021, 26, 3967. [Google Scholar] [CrossRef]
- Elfadaly, A.; Attia, W.; Qelichi, M.M.; Murgante, B.; Lasaponara, R. Management of Cultural Heritage Sites Using Remote Sensing Indices and Spatial Analysis Techniques. Surv. Geophys. 2018, 39, 1347–1377. [Google Scholar] [CrossRef]
- Fatorić, S.; Seekamp, E. Securing the Future of Cultural Heritage by Identifying Barriers to and Strategizing Solutions for Preservation under Changing Climate Conditions. Sustainability 2017, 9, 2143. [Google Scholar] [CrossRef]
- Bertolin, C. Preservation of Cultural Heritage and Resources Threatened by Climate Change. Geosciences 2019, 9, 250. [Google Scholar] [CrossRef]
- Bienvenido-Huertas, D.; León-Muñoz, M.; Martín-del-Río, J.J.; Rubio-Bellido, C. Analysis of climate change impact on the preservation of heritage elements in historic buildings with a deficient indoor microclimate in warm regions. Build. Environ. 2021, 200, 107959. [Google Scholar] [CrossRef]
- Paschalidou, E.; Fafet, C.; Milios, L. A Strong Sustainability Framework for Digital Preservation of Cultural Heritage: Introducing the Eco-Sufficiency Perspective. Heritage 2022, 5, 1066–1088. [Google Scholar] [CrossRef]
- Hsu, H.-H.; Huang, J.-S. Passive Environmental Control at Neighborhood and Block Scales for Conservation of Historic Settlements: The Case Study of Huatzai Village in Wang-An, Taiwan. Sustainability 2022, 14, 11840. [Google Scholar] [CrossRef]
- Hu, X.; Assaad, R.H. A BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization by integrating autonomous robotics, LiDAR-based 3D mobile mapping, IoT sensing, and indoor positioning technologies. J. Build. Eng. 2024, 86, 108901. [Google Scholar] [CrossRef]
- Addabbo, T.; Fort, A.; Mugnaini, M.; Panzardi, E.; Pozzebon, A.; Vignoli, V. A city-scale IoT architecture for monumental structures monitoring. Meas. J. Int. Meas. Confed. 2019, 131, 349–357. [Google Scholar] [CrossRef]
- Mitro, N.; Krommyda, M.; Amditis, A. Smart Tags: IoT Sensors for Monitoring the Micro-Climate of Cultural Heritage Monuments. Appl. Sci. 2022, 12, 2315. [Google Scholar] [CrossRef]
- Bruno, S.; Galantucci, R.A.; Musicco, A. Decay detection in historic buildings through image-based deep learning. Vitruvio 2023, 8, 6–17. [Google Scholar] [CrossRef]
- Croce, V.; Caroti, G.; Piemonte, A.; De Luca, L.; Véron, P. H-BIM and Artificial Intelligence: Classification of Architectural Heritage for Semi-Automatic Scan-to-BIM Reconstruction. Sensors 2023, 23, 2497. [Google Scholar] [CrossRef]
- Marzouk, M.; Atef, M. Assessment of Indoor Air Quality in Academic Buildings Using IoT and Deep Learning. Sustainability 2022, 14, 7015. [Google Scholar] [CrossRef]
- Mishra, M.; Lourenço, P.B. Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review. J. Cult. Herit. 2024, 66, 536–550. [Google Scholar] [CrossRef]
- Elabd, N.M.; Mansour, Y.M.; Khodier, L.M. Utilizing innovative technologies to achieve resilience in heritage buildings preservation. Dev. Built Environ. 2021, 8, 100058. [Google Scholar] [CrossRef]
- Metwally, E.A.; Ismail, M.R.; Farid, A.A. Advancing Building Assessment Tools: Achieving Sustainable Development Goals through the Fusion of Internet of Things Occupant-Centric Principles and Sustainable Practices. Buildings 2024, 14, 1798. [Google Scholar] [CrossRef]
- Kang, T.W.; Mo, Y. A comprehensive digital twin framework for building environment monitoring with emphasis on real-time data connectivity and predictability. Dev. Built Environ. 2024, 17, 100309. [Google Scholar] [CrossRef]
- Franco, A.; Crisostomi, E.; Dalmiani, S.; Poletti, R. Synergy in Action: Integrating Environmental Monitoring, Energy Efficiency, and IoT for Safer Shared Buildings. Buildings 2024, 14, 1077. [Google Scholar] [CrossRef]
- The National Institute of Building Sciences. Sustainable Historic Preservation. Available online: https://www.wbdg.org/design-objectives/historic-preservation/sustainable-historic-preservation (accessed on 19 September 2024).
- Siccardi, S.; Villa, V. Trends in Adopting BIM, IoT and DT for Facility Management: A Scientometric Analysis and Keyword Co-Occurrence Network Review. Buildings 2023, 13, 15. [Google Scholar] [CrossRef]
- Karatzas, S.; Papageorgiou, G.; Lazari, V.; Bersimis, S.; Fousteris, A.; Economou, P.; Chassiakos, A. A text analytic framework for gaining insights on the integration of digital twins and machine learning for optimizing indoor building environmental performance. Dev. Built Environ. 2024, 18, 100386. [Google Scholar] [CrossRef]
- Page, M.J.; Moher, D.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ 2021, 372, n160. [Google Scholar] [CrossRef]
- Arachchige, G.R.P.; Thorstensen, E.B.; Coe, M.; McKenzie, E.J.; O’Sullivan, J.M.; Pook, C.J. LC-MS/MS quantification of fat soluble vitamers—A systematic review. Anal. Biochem. 2021, 613, 113980. [Google Scholar] [CrossRef]
- Baas, J.; Schotten, M.; Plume, A.; Côté, G.; Karimi, R. Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quant. Sci. Stud. 2020, 1, 377–386. [Google Scholar] [CrossRef]
- Zhu, J.; Liu, W. A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics 2020, 123, 321–335. [Google Scholar] [CrossRef]
- UN. THE 17 GOALS|Sustainable Development. Available online: https://sdgs.un.org/goals (accessed on 15 November 2024).
- Bramer, W.M.; Giustini, D.M.; de Jonge, G.B.; Holland, L.; Bekhuis, T. De-duplication of database search results for systematic reviews in EndNote. J. Med. Libr. Assoc. JMLA 2016, 104, 240–243. [Google Scholar] [CrossRef]
- Aria, M.; Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
- Wei, W.; Jiang, Z. A bibliometrix-based visualization analysis of international studies on conversations of people with aphasia: Present and prospects. Heliyon 2023, 9, e16839. [Google Scholar] [CrossRef]
- Abdul Hamid, A.; Johansson, D.; Bagge, H. Ventilation measures for heritage office buildings in temperate climate for improvement of energy performance and IEQ. Energy Build. 2020, 211, 109822. [Google Scholar] [CrossRef]
- Alaidroos, A.; Almaimani, A.; Baik, A.; Al-Amodi, M.; Rahaman, K.R. Are historical buildings more adaptive to minimize the risks of airborne transmission of viruses and public health? A study of the hazzazi house in Jeddah (Saudi Arabia). Int. J. Environ. Res. Public Health 2021, 18, 3601. [Google Scholar] [CrossRef] [PubMed]
- Aldoasri, M.A.; Darwish, S.S.; Adam, M.A.; Elmarzugi, N.A.; Ahmed, S.M. Protecting of marble stone facades of historic buildings using multifunctional TiO2 nanocoatings. Sustainability 2017, 9, 2002. [Google Scholar] [CrossRef]
- Alizzio, D.; De Capua, C.; Fulco, G.; Lugarà, M.; Palco, V.; Ruffa, F. IOT enviromental quality monitoring in smart buildings in presence of measurement uncertainty: A decision making approach. Acta IMEKO 2023, 12, 1–8. [Google Scholar] [CrossRef]
- Aste, N.; Adhikari, R.S.; Buzzetti, M.; Della Torre, S.; Del Pero, C.; Huerto, C.H.E.; Leonforte, F. Microclimatic monitoring of the Duomo (Milan Cathedral): Risks-based analysis for the conservation of its cultural heritage. Build. Environ. 2019, 148, 240–257. [Google Scholar] [CrossRef]
- Balocco, C.; Colaianni, A. Modelling of reversible plant system operations in a cultural heritage school building for indoor thermal comfort. Sustainability 2018, 10, 3776. [Google Scholar] [CrossRef]
- Berto, L.; Doria, A.; Faccio, P.; Saetta, A.; Talledo, D. Vulnerability Analysis of Built Cultural Heritage: A Multidisciplinary Approach for Studying the Palladio’s Tempietto Barbaro. Int. J. Archit. Herit. 2017, 11, 773–790. [Google Scholar] [CrossRef]
- Bertolin, C.; Camuffo, D.; Bighignoli, I. Past reconstruction and future forecast of domains of indoor relative humidity fluctuations calculated according to EN 15757:2010. Energy Build. 2015, 102, 197–206. [Google Scholar] [CrossRef]
- Bienvenido-Huertas, D.; Nieto-Julián, J.E.; Moyano, J.J.; Macías-Bernal, J.M.; Castro, J. Implementing Artificial Intelligence in H-BIM Using the J48 Algorithm to Manage Historic Buildings. Int. J. Archit. Herit. 2020, 14, 1148–1160. [Google Scholar] [CrossRef]
- Boesgaard, C.; Hansen, B.V.; Kejser, U.B.; Mollerup, S.H.; Ryhl-Svendsen, M.; Torp-Smith, N. Prediction of the indoor climate in cultural heritage buildings through machine learning: First results from two field tests. Herit. Sci. 2022, 10, 176. [Google Scholar] [CrossRef]
- Bonazza, A.; Sardella, A.; Kaiser, A.; Cacciotti, R.; De Nuntiis, P.; Hanus, C.; Maxwell, I.; Drdácký, T.; Drdácký, M. Safeguarding cultural heritage from climate change related hydrometeorological hazards in Central Europe. Int. J. Disaster Risk Reduct. 2021, 63, 102455. [Google Scholar] [CrossRef]
- Califano, A.; Baiesi, M.; Bertolin, C. Analysing the Main Standards for Climate-Induced Mechanical Risk in Heritage Wooden Structures: The Case of the Ringebu and Heddal Stave Churches (Norway). Atmosphere 2022, 13, 791. [Google Scholar] [CrossRef]
- Carpio, M.; Ortega, J.; Prieto, A.J. Expert panel on in-situ visual inspections for masonry churches maintenance stage. J. Civ. Eng. Manag. 2021, 27, 454–471. [Google Scholar] [CrossRef]
- Carpio, M.; Prieto, A.J. Expert panel, preventive maintenance of heritage buildings and fuzzy logic system: An application in valdivia, Chile. Sustainability 2021, 13, 6922. [Google Scholar] [CrossRef]
- Casprini, E.; Passoni, C.; Marini, A.; Bartoli, G. DEMSA Protocol: Deterioration Effect Modelling for Structural Assessment of RC Buildings. Buildings 2022, 12, 574. [Google Scholar] [CrossRef]
- Ceccarelli, L.; Bevilacqua, M.G.; Caroti, G.; Castiglia, R.B.F.; Croce, V. Semantic segmentation through Artificial Intelligence from raw point clouds to H-BIM representation. Disegnarecon 2023, 16, 171–178. [Google Scholar] [CrossRef]
- Coelho, G.B.A.; Silva, H.E.; Henriques, F.M.A. Impact of climate change on cultural heritage: A simulation study to assess the risks for conservation and thermal comfort. Int. J. Glob. Warm. 2019, 19, 382–406. [Google Scholar] [CrossRef]
- Costa-Carrapiço, I.; González, J.N.; Raslan, R.; Sánchez-Guevara, C.; Redondas Marrero, M.D. Understanding thermal comfort in vernacular dwellings in Alentejo, Portugal: A mixed-methods adaptive comfort approach. Build. Environ. 2022, 217, 109084. [Google Scholar] [CrossRef]
- Daengprathum, N.; Onchang, R.; Nakhapakorn, K.; Robert, O.; Tipayarom, A.; Sturm, P.J. Estimation of Effects of Air Pollution on the Corrosion of Historical Buildings in Bangkok. Environ. Nat. Resour. J. 2022, 20, 505–514. [Google Scholar] [CrossRef]
- De Marco, A.; Screpanti, A.; Mircea, M.; Piersanti, A.; Proietti, C.; Fornasier, M.F. High resolution estimates of the corrosion risk for cultural heritage in Italy. Environ. Pollut. 2017, 226, 260–267. [Google Scholar] [CrossRef]
- Dişli, G.; Kilit, R.M. Risk Management and Preventive Conservation of Cultural Heritage: The Case of Karaman Hatuniye Madrasa and Karaman City Museum. J. Integr. Disaster Risk Manag. 2024, 14, 49–74. [Google Scholar] [CrossRef]
- Elnaggar, A.; Said, M.; Kraševec, I.; Said, A.; Grau-Bove, J.; Moubarak, H. Risk analysis for preventive conservation of heritage collections in Mediterranean museums: Case study of the museum of fine arts in Alexandria (Egypt). Herit. Sci. 2024, 12, 59. [Google Scholar] [CrossRef]
- Esteban-Cantillo, O.J.; Menendez, B.; Quesada, B. Climate change and air pollution impacts on cultural heritage building materials in Europe and Mexico. Sci. Total Environ. 2024, 921, 170945. [Google Scholar] [CrossRef]
- Falk, M.; Hagsten, E. A management perspective on threats to Cultural World Heritage Sites. Int. J. Herit. Stud. 2023, 29, 167–183. [Google Scholar] [CrossRef]
- Fan, J.; Chen, Y.; Zheng, L. Artificial Intelligence for Routine Heritage Monitoring and Sustainable Planning of the Conservation of Historic Districts: A Case Study on Fujian Earthen Houses (Tulou). Buildings 2024, 14, 1915. [Google Scholar] [CrossRef]
- Fiorini, L.; Conti, A.; Pellis, E.; Bonora, V.; Masiero, A.; Tucci, G. Machine Learning-Based Monitoring for Planning Climate-Resilient Conservation of Built Heritage. Drones 2024, 8, 249. [Google Scholar] [CrossRef]
- Florescu, O.; Ichim, P.; Sfîcă, L.; Kadhim-Abid, A.L.; Sandu, I.; Nănescu, M. Risk Assessment of Artifact Degradation in a Museum, Based on Indoor Climate Monitoring—Case Study of “Poni-Cernătescu” Museum from Iași City. Appl. Sci. 2022, 12, 3313. [Google Scholar] [CrossRef]
- Frasca, F.; Verticchio, E.; Cornaro, C.; Siani, A.M. Performance assessment of hygrothermal modelling for diagnostics and conservation in an Italian historical church. Build. Environ. 2021, 193, 107672. [Google Scholar] [CrossRef]
- Galantucci, R.A.; Musicco, A.; Verdoscia, C.; Fatiguso, F. Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets. Int. J. Archit. Herit. 2023, 1–19. [Google Scholar] [CrossRef]
- García-Florentino, C.; Maguregui, M.; Carrero, J.A.; Morillas, H.; Arana, G.; Madariaga, J.M. Development of a cost effective passive sampler to quantify the particulate matter depositions on building materials over time. J. Clean. Prod. 2020, 268, 122134. [Google Scholar] [CrossRef]
- Gibeaux, S.; Martínez-Garrido, M.I.; Vázquez, P.; Thomachot-Schneider, C.; Fort, R. Wireless environmental monitoring coupled to NDT for decay risk analysis (at St. Joseph Chapel in Reims, France). Sens. Actuators A Phys. 2018, 272, 102–113. [Google Scholar] [CrossRef]
- Giglio, F.; Frontera, P.; Malara, A.; Armocida, F. Materials and Climate Change: A Set of Indices as the Benchmark for Climate Vulnerability and Risk Assessment for Tangible Cultural Heritage in Europe. Sustainability 2024, 16, 2067. [Google Scholar] [CrossRef]
- Haznedar, B.; Bayraktar, R.; Ozturk, A.E.; Arayici, Y. Implementing PointNet for point cloud segmentation in the heritage context. Herit. Sci. 2023, 11, 2. [Google Scholar] [CrossRef]
- Hedayatnia, H.; Top, S.; Caluwaerts, S.; Kotova, L.; Steeman, M.; Van Den Bossche, N. Evaluation of alaro-0 and remo regional climate models over iran focusing on building material degradation criteria. Buildings 2021, 11, 376. [Google Scholar] [CrossRef]
- Ilieș, A.; Caciora, T.; Marcu, F.; Berdenov, Z.; Ilieș, G.; Safarov, B.; Hodor, N.; Grama, V.; Shomali, M.A.A.; Ilies, D.C.; et al. Analysis of the Interior Microclimate in Art Nouveau Heritage Buildings for the Protection of Exhibits and Human Health. Int. J. Environ. Res. Public Health 2022, 19, 16599. [Google Scholar] [CrossRef]
- Ladiana, D.; Di Sivo, M. Programmed conservation of historical and architectural heritage. tools for optimising a process based on knowledge and information. Int. J. Des. Nat. Ecodyn. 2019, 14, 229–240. [Google Scholar] [CrossRef]
- Leijonhufvud, G. Making sense of climate risk information: The case of future indoor climate risks in Swedish churches. Clim. Risk Manag. 2016, 13, 76–87. [Google Scholar] [CrossRef]
- Leissner, J.; Kilian, R.; Kotova, L.; Jacob, D.; Mikolajewicz, U.; Broström, T.; Ashley-Smith, J.; Schellen, H.L.; Martens, M.; Van Schijndel, J.; et al. Climate for culture: Assessing the impact of climate change on the future indoor climate in historic buildings using simulations. Herit. Sci. 2015, 3, 38. [Google Scholar] [CrossRef]
- Leon, I.; Pérez, J.J.; Senderos, M. Advanced techniques for fast and accurate heritage digitisation in multiple case studies. Sustainability 2020, 12, 6068. [Google Scholar] [CrossRef]
- Lerario, A.; Varasano, A. An IoT smart infrastructure for S. Domenico Church in Matera’s “Sassi”: A multiscale perspective to built heritage conservation. Sustainability 2020, 12, 6553. [Google Scholar] [CrossRef]
- Lerma, C.; Borràs, J.G.; Mas, Á.; Torner, M.E.; Vercher, J.; Gil, E. Evaluation of hygrothermal behaviour in heritage buildings through sensors, CFD modelling and IRT. Sensors 2021, 21, 566. [Google Scholar] [CrossRef] [PubMed]
- Li, M. Disaster risk management of cultural heritage: A global scale analysis of characteristics, multiple hazards, lessons learned from historical disasters, and issues in current DRR measures in world heritage sites. Int. J. Disaster Risk Reduct. 2024, 110, 104633. [Google Scholar] [CrossRef]
- Lin, H.; Huang, L.; Chen, Y.; Zheng, L.; Huang, M.; Chen, Y. Research on the Application of CGAN in the Design of Historic Building Facades in Urban Renewal—Taking Fujian Putian Historic Districts as an Example. Buildings 2023, 13, 1478. [Google Scholar] [CrossRef]
- Luo, S.; Wang, H. Digital Twin Research on Masonry–Timber Architectural Heritage Pathology Cracks Using 3D Laser Scanning and Deep Learning Model. Buildings 2024, 14, 1129. [Google Scholar] [CrossRef]
- Marcelli, A.; Sebastianelli, M.; Conte, A.; Lucci, F.; Della Ventura, G. Micro-climatic investigation and particulate detection in indoor environments: The case of the historical museum of Bersaglieri in Rome. Rend. Lincei 2020, 31, 807–817. [Google Scholar] [CrossRef]
- Martinelli, L.; Calcerano, F.; Adinolfi, F.; Chianetta, D.; Gigliarelli, E. Open HBIM-IoT Monitoring Platform for the Management of Historical Sites and Museums. An Application to the Bourbon Royal Site of Carditello. Int. J. Archit. Herit. 2023, 1–18. [Google Scholar] [CrossRef]
- Marzouk, M.; ElSharkawy, M.; Elsayed, P.; Eissa, A. Resolving deterioration of heritage building elements using an expert system. Int. J. Build. Pathol. Adapt. 2020, 38, 721–735. [Google Scholar] [CrossRef]
- Matrone, F.; Martini, M. Transfer learning and performance enhancement techniques for deep semantic segmentation of built heritage point clouds. Virtual Archaeol. Rev. 2021, 12, 73–84. [Google Scholar] [CrossRef]
- Metals, M.; Lesinskis, A.; Borodinecs, A.; Turauskis, K. Study on indoor air temperature and moisture behaviour in historical churches. Energy Build. 2024, 310, 114083. [Google Scholar] [CrossRef]
- Mohamed El Abd, N. Smart monitoring solution through internet of things utilization to achieve resilient preservation. Ain Shams Eng. J. 2023, 14, 102176. [Google Scholar] [CrossRef]
- Moreno, M.; Prieto, A.J.; Ortiz, R.; Cagigas-Muñiz, D.; Becerra, J.; Garrido-Vizuete, M.A.; Segura, D.; Macías-Bernal, J.M.; Chávez, M.J.; Ortiz, P. Preventive Conservation and Restoration Monitoring of Heritage Buildings Based on Fuzzy Logic. Int. J. Archit. Herit. 2023, 17, 1153–1170. [Google Scholar] [CrossRef]
- Mouffok, M.; Zemmouri, N.; Aidaoui, L.; Lasbet, Y.; De Herde, A. Effects of building morphologies on CO2 air pollution case study: The vernacular urban fabric, city of Ghardaïa (Algeria). Asian J. Civ. Eng. 2017, 18, 1–19. [Google Scholar]
- Mustafin, M.; Nasrullah, M.; Abboud, M. 3D Modeling of Sidon Sea Castle Utilizing Terrestrial Laser Scanner Combined with Photogrammetry. Int. J. Geoinform. 2024, 20, 28–39. [Google Scholar] [CrossRef]
- Nunes, L.J.R.; Curado, A.; Lopes, S.I. Understanding Seasonal Indoor Radon Variability from Data Collected with a LoRa-Enabled IoT Edge Device. Appl. Sci. 2023, 13, 4735. [Google Scholar] [CrossRef]
- O’Neill, S.; Tett, S.F.B.; Donovan, K. Extreme rainfall risk and climate change impact assessment for Edinburgh World Heritage sites. Weather Clim. Extrem. 2022, 38, 100514. [Google Scholar] [CrossRef]
- O’Brien, G.; O’Keefe, P.; Jayawickrama, J.; Jigyasu, R. Developing a model for building resilience to climate risks for cultural heritage. J. Cult. Herit. Manag. Sustain. Dev. 2015, 5, 99–114. [Google Scholar] [CrossRef]
- Pereira, L.D.; Tavares, V.; Soares, N. Up-to-date challenges for the conservation, rehabilitation and energy retrofitting of higher education cultural heritage buildings. Sustainability 2021, 13, 2061. [Google Scholar] [CrossRef]
- Perez-Garcia, A.; Guardiola, A.P.; Gómez-Martínez, F.; Guardiola-Víllora, A. Energy-saving potential of large housing stocks of listed buildings, case study: L’Eixample of Valencia. Sustain. Cities Soc. 2018, 42, 59–81. [Google Scholar] [CrossRef]
- Prieto, A.J.; Verichev, K.; Silva, A.; de Brito, J. On the impacts of climate change on the functional deterioration of heritage buildings in South Chile. Build. Environ. 2020, 183, 107138. [Google Scholar] [CrossRef]
- Prizeman, O.; Pezzica, C.; Taher, A.; Boughanmi, M. Networking historic environmental standards to address modern challenges for sustainable conservation in HBIM. Appl. Sci. 2020, 10, 1283. [Google Scholar] [CrossRef]
- Quesada-Ganuza, L.; Garmendia, L.; Alvarez, I.; Roji, E. Vulnerability assessment and categorization against heat waves for the Bilbao historic area. Sustain. Cities Soc. 2023, 98, 104805. [Google Scholar] [CrossRef]
- Radulescu, C.; Stihi, C.; Ion, R.M.; Dulama, I.D.; Stanescu, S.G.; Stirbescu, R.M.; Teodorescu, S.; Gurgu, I.V.; Let, D.D.; Olteanu, L.; et al. Seasonal variability in the composition of particulate matter and the microclimate in cultural heritage areas. Atmosphere 2019, 10, 595. [Google Scholar] [CrossRef]
- Rajčić, V.; Skender, A.; Damjanović, D. An innovative methodology of assessing the climate change impact on cultural heritage. Int. J. Archit. Herit. 2018, 12, 21–35. [Google Scholar] [CrossRef]
- Rieser, A.; Pfluger, R.; Troi, A.; Herrera-Avellanosa, D.; Thomsen, K.E.; Rose, J.; Arsan, Z.D.; Akkurt, G.G.; Kopeinig, G.; Guyot, G.; et al. Integration of energy-efficient ventilation systems in historic buildings—Review and proposal of a systematic intervention approach. Sustainability 2021, 13, 2325. [Google Scholar] [CrossRef]
- Rodrigues, F.; Cotella, V.; Rodrigues, H.; Rocha, E.; Freitas, F.; Matos, R. Application of Deep Learning Approach for the Classification of Buildings’ Degradation State in a BIM Methodology. Appl. Sci. 2022, 12, 7403. [Google Scholar] [CrossRef]
- Ruggiero, G.; Marmo, R.; Nicolella, M. A methodological approach for assessing the safety of historic buildings’ Façades. Sustainability 2021, 13, 2812. [Google Scholar] [CrossRef]
- Sánchez, B.; de Oliveira Souza, M.; Vilanova, O.; Canela, M.C. Volatile organic compounds in the Spanish National Archaeological Museum: Four years of chemometric analysis. Build. Environ. 2020, 174, 106780. [Google Scholar] [CrossRef]
- Saraiva, N.B.; Pereira, L.D.; Gaspar, A.R.; Costa, J.J. Measurement of particulate matter in a heritage building using optical counters: Long-term and spatial analyses. Sci. Total Environ. 2023, 862, 160747. [Google Scholar] [CrossRef]
- Scatigno, C.; Moricca, C.; Tortolini, C.; Favero, G. The influence of environmental parameters in the biocolonization of the Mithraeum in the roman masonry of casa di Diana (Ostia Antica, Italy). Environ. Sci. Pollut. Res. 2016, 23, 13403–13412. [Google Scholar] [CrossRef]
- Shillito, L.M.; Namdeo, A.; Bapat, A.V.; Mackay, H.; Haddow, S.D. Analysis of fine particulates from fuel burning in a reconstructed building at Çatalhöyük World Heritage Site, Turkey: Assessing air pollution in prehistoric settled communities. Environ. Geochem. Health 2022, 44, 1033–1048. [Google Scholar] [CrossRef]
- Silva, H.E.; Henriques, F.M.A. Preventive conservation of historic buildings in temperate climates. The importance of a risk-based analysis on the decision-making process. Energy Build. 2015, 107, 26–36. [Google Scholar] [CrossRef]
- Silva, H.E.; Henriques, F.M.A. Hygrothermal analysis of historic buildings: Statistical methodologies and their applicability in temperate climates. Struct. Surv. 2016, 34, 12–23. [Google Scholar] [CrossRef]
- Silva, H.E.; Henriques, F.M.A. The impact of tourism on the conservation and IAQ of cultural heritage: The case of the Monastery of Jerónimos (Portugal). Build. Environ. 2021, 190, 160747. [Google Scholar] [CrossRef]
- Sitzia, F.; Peters, M.J.H.; Lisci, C. Climate change and its outcome on the archaeological areas and their building materials. The case study of Tharros (Italy). Digit. Appl. Archaeol. Cult. Herit. 2022, 25, e00226. [Google Scholar] [CrossRef]
- Tavolare, R.; Cabrera, E.; Verdoscia, C.; Buldo, M. A point cloud classification method for the Scan-to-BIM process in Architectural Heritage. Disegnarecon 2023, 16, 201–208. [Google Scholar] [CrossRef]
- Torres-González, M.; Prieto, A.J.; Alejandre, F.J.; Blasco-López, F.J. Digital management focused on the preventive maintenance of World Heritage Sites. Autom. Constr. 2021, 129, 103813. [Google Scholar] [CrossRef]
- Tysiac, P.; Sieńska, A.; Tarnowska, M.; Kedziorski, P.; Jagoda, M. Combination of terrestrial laser scanning and UAV photogrammetry for 3D modelling and degradation assessment of heritage building based on a lighting analysis: Case study—St. Adalbert Church in Gdansk, Poland. Herit. Sci. 2023, 11, 53. [Google Scholar] [CrossRef]
- Vandenabeele, L.; Loverdos, D.; Pfister, M.; Sarhosis, V. Deep Learning for the Segmentation of Large-Scale Surveys of Historic Masonry: A New Tool for Building Archaeology Applied at the Basilica of St Anthony in Padua. Int. J. Archit. Herit. 2023, 18, 1749–1761. [Google Scholar] [CrossRef]
- Varas-Muriel, M.J.; Fort, R.; Martínez-Garrido, M.I.; Zornoza-Indart, A.; López-Arce, P. Fluctuations in the indoor environment in Spanish rural churches and their effects on heritage conservation: Hygro-thermal and CO2 conditions monitoring. Build. Environ. 2014, 82, 97–109. [Google Scholar] [CrossRef]
- Wojtkowska, M.; Kedzierski, M.; Delis, P. Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures. Meas. J. Int. Meas. Confed. 2021, 167, 108291. [Google Scholar] [CrossRef]
- Yan, L.; Chen, Y.; Zheng, L.; Zhang, Y. Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: A case study of the classical gardens of Suzhou. Herit. Sci. 2024, 12, 72. [Google Scholar] [CrossRef]
- Yiğit, A.Y.; Uysal, M. Automatic crack detection and structural inspection of cultural heritage buildings using UAV photogrammetry and digital twin technology. J. Build. Eng. 2024, 94, 109952. [Google Scholar] [CrossRef]
- Živković, V.; Džikić, V. Return to basics—Environmental management for museum collections and historic houses. Energy Build. 2015, 95, 116–123. [Google Scholar] [CrossRef]
- Metreau, E.; Young, K.E.; Eapen, S.G. World Bank Country Classifications by Income Level for 2024–2025. Available online: https://blogs.worldbank.org/en/opendata/world-bank-country-classifications-by-income-level-for-2024-2025 (accessed on 16 November 2024).
- Arsiwala, A.; Elghaish, F.; Zoher, M. Digital twin with Machine learning for predictive monitoring of CO2 equivalent from existing buildings. Energy Build. 2023, 284, 112851. [Google Scholar] [CrossRef]
Criterion Type | Inclusion Criteria | Exclusion Criteria |
---|---|---|
Research area | Architecture or Engineering or Environmental Science or Social Sciences | Not related to Architecture or Engineering or Environmental Science or Social Sciences |
Database | SCOPUS | Outside SCOPUS |
Topic | Heritage building; risk assessment; air pollution; climate risk; IoT; Artificial Intelligence | Not related to heritage building |
Year of publication | 2014–2024 | Outside the set range |
Publication type | Peer-reviewed academic paper | Other types of publication (e.g., conference proceedings and books) |
Language | English | Other languages |
Keywords | Document Found (N) | Advanced Query | |
---|---|---|---|
Heritage Building | Risk assessment | 314 | TITLE-ABS-KEY (heritage AND building AND risk AND assessment) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (SRCTYPE, “j”)) |
Air pollution | 80 | TITLE-ABS-KEY (heritage AND building AND air AND pollution) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (SRCTYPE, “j”)) | |
Climate risk | 117 | TITLE-ABS-KEY (heritage AND building AND climate AND risk) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (SRCTYPE, “j”)) | |
Artificial Intelligence | 51 | TITLE-ABS-KEY (heritage AND building AND artificial AND intelligence) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (SRCTYPE, “j”)) | |
IoT | 21 | TITLE-ABS-KEY (heritage AND building AND iot) AND PUBYEAR > 2013 AND PUBYEAR < 2025 AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO ( SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”)) AND (LIMIT-TO (SRCTYPE, “j”)) | |
Total | 583 |
Year | Monitored Parameters | Indoor | Outdoor | Source |
---|---|---|---|---|
2014 | relative humidity, temperature, wind speed, CO, SO2, NO, NO2, O3 | - | ✓ | [11] |
2014 | CO2, relative humidity, temperature | ✓ | ✓ | [122] |
2015 | relative humidity, temperature | ✓ | ✓ | [51] |
2015 | relative humidity, temperature, NO2, SO2, CO2, O3, PM2.5, microflora | ✓ | ✓ | [3] |
2015 | temperature, relative humidity, total precipitation (normal rain), wind speed, wind direction, global radiation, diffuse radiation, global counter radiation, cloud coverage, ground temperature, ground reflectance, air pressure | ✓ | ✓ | [81] |
2015 | relative humidity, temperature | ✓ | ✓ | [113] |
2015 | relative humidity, temperature, infrared radiation | ✓ | ✓ | [126] |
2016 | relative humidity, temperature, CO, CO2, light | ✓ | - | [112] |
2016 | relative humidity, temperature | ✓ | - | [115] |
2017 | air pollution (SO2, HNO3, O3, PM10) and meteorological conditions (temperature, precipitation, relative humidity) | - | ✓ | [63] |
2017 | CO2, temperature, wind speed and solar radiation | - | ✓ | [95] |
2018 | temperature, humidity, pressure, O3, NO, NO2, SO2 | - | ✓ | [74] |
2018 | relative humidity, temperature | ✓ | ✓ | [106] |
2019 | relative humidity, temperature | ✓ | ✓ | [48] |
2019 | relative humidity, temperature | ✓ | - | [60] |
2019 | temperature, relative humidity, apparent temperature, PM10, PM2.5 | ✓ | ✓ | [105] |
2020 | relative humidity, temperature, CO2, air change rates | - | ✓ | [44] |
2020 | PM | - | ✓ | [73] |
2020 | relative humidity, temperature, PM10, PM2.5 | ✓ | - | [88] |
2020 | VOCs | ✓ | ✓ | [110] |
2021 | relative humidity, temperature | ✓ | - | [19] |
2021 | relative humidity, temperature, volumetric water content | ✓ | ✓ | [71] |
2021 | relative humidity, temperature | ✓ | ✓ | [84] |
2021 | relative humidity, temperature | ✓ | ✓ | [116] |
2022 | relative humidity | ✓ | - | [53] |
2022 | relative humidity, temperature | ✓ | ✓ | [55] |
2022 | outdoor and indoor air temperature, relative humidity, globe and surface temperature, air velocity | ✓ | ✓ | [61] |
2022 | SO2, NO2, O3, and PM10 | - | ✓ | [62] |
2022 | relative humidity, temperature | ✓ | ✓ | [70] |
2022 | temperature, humidity, the amount of natural light and artificial light, CO2, PM2.5, PM10, HCHO, VOC, O2, SO2, O3, NO2, NO, H2S, CO, CH4 | ✓ | - | [78] |
2022 | relative humidity, temperature | ✓ | ✓ | [24] |
2022 | PM2.5 | ✓ | - | [113] |
2023 | CO2, PM10, PM2.5, temperature, relative humidity | ✓ | ✓ | [47] |
2023 | Rn, relative humidity, temperature | ✓ | - | [97] |
2023 | PM10, PM2.5 | ✓ | ✓ | [111] |
2024 | temperature, relative humidity, concentrations of NO2, SO2,O3, CO2, acetic, formic acid | ✓ | ✓ | [65] |
2024 | air pollution (SO2, HNO3, O3, PM10) and meteorological conditions (temperature, precipitation, relative humidity, wind) | - | ✓ | [66] |
2024 | relative humidity, temperature | ✓ | ✓ | [92] |
Source | Risk Type | Data Source | Framework | Methodology | Technology |
---|---|---|---|---|---|
[23] | Structural integrity | Sensor data from IoT infrastructure | IoT architecture for cultural heritage monitoring | Development and testing of IoT sensors | Wireless sensor networks (WSN) |
[47] | Microclimatic risk and energy efficiency | Monitoring data and simulation results | Energy efficiency and environmental quality assessment | Dynamic energy simulations and in-field measurements | IoT sensors, CFD, dynamic energy models |
[52] | Implementation of AI in H-BIM using J48 algorithm for building management | H-BIM model of a case study | AI-based decision-making framework for building management | Application of decision trees and AI for building management | J48 decision tree algorithm, H-BIM platform, Geometric Description Language (GDL) scripting |
[53] | Prediction of indoor climate through ML | ML datasets from climate monitoring | ML model XGBoost for prediction of relative humidity | Time-series analysis with ML algorithms | ML, XGBoost model, climate control systems |
[25] | Structural decay and damage detection | Image dataset from historic buildings | DL for decay detection | Image-based DL using Mask R-CNN | Mask R-CNN, Computer Vision |
[56] | Hygrothermal assessment | Hygrothermal monitoring and IoT sensor data | IoT-based environmental monitoring framework | IoT sensor deployment and monitoring of environmental variables | IoT sensors, Heating, Ventilation and Air Conditioning (HVAC) systems, energy-saving models |
[59] | AI-based segmentation of point clouds for H-BIM | Point cloud data from a case study | AI-based segmentation with H-BIM framework | Semantic segmentation with multi-level and multi-resolution | ML, point cloud segmentation, H-BIM tools |
[26] | Structural integrity and conservation planning | 3D survey data from case study | AI and H-BIM for heritage reconstruction | Semantic segmentation and 3D reconstruction using ML | H-BIM, Scan-to-BIM, ML, Geomagic Design X |
[68] | Wooden heritage structure damage detection | YOLOv8, preservation data | Computer Vision, AI-driven preservation | YOLOv8 model for damage detection in wooden structures | Computer vision, Object detection technology |
[72] | Damage segmentation and decay mapping | Photogrammetric and 3D point cloud data from architectural heritage case studies | Semi-automatic decay detection and mapping using ML (supervised and unsupervised clustering) for 3D models | Combination of photogrammetry and ML for unsupervised clustering of point clouds and supervised texture-based ML to detect decay. | ML techniques (Random Forest, hierarchical clustering), photogrammetry, and 3D point cloud processing tools |
[76] | Automated segmentation and classification of heritage buildings using 3D point cloud data for HBIM integration | Point cloud data collected from heritage buildings, processed using PointNet | H-BIM enhanced by PointNet for automated segmentation and classification of heritage building elements | AI-based segmentation using PointNet applied to 140 rooms across 19 historical buildings. The training used five segmentation labels: walls, roofs, floors, doors, windows | PointNet DL architecture integrated with HBIM for efficient heritage building information modeling using 3D point cloud data |
[79] | Physical deterioration, material decay | Knowledge-based conservation studies | Programmed conservation approach | Analysis, monitoring, diagnosis, and data cataloging | BIM, H-BIM |
[83] | Structural degradation | IoT smart infrastructure monitoring, Wireless sensor networks (WSN) | Preventive conservation and resilience enhancement using Structural Health Monitoring systems (SHM) | Sensor-based monitoring, IoT integration | WSN, FIWARE platform, SHM, IoT devices |
[85] | Multiple natural and human-induced hazards affecting World Heritage (WH) sites globally | Historical disaster records, management plans, and disaster risk reduction (DRR) strategies from 48 WH sites across 41 countries | Disaster risk management (DRM) for cultural heritage, integrating historical lessons with preventive strategies | Analysis of past disasters, hazard mapping, vulnerability assessment, and development of DRM plans | Geographic Information System (GIS) for hazard mapping, satellite imagery, early warning systems, and IoT sensors for disaster monitoring |
[86] | Architectural style inconsistency and urban renewal | Image data of facades, Conditional Generative Adversarial Network (CGAN)-generated facade styles, field surveys | Use of CGAN for facade design and urban cultural regeneration | CGAN-based style generation, image processing, and ML for facade design | CGAN, image processing software, data labeling tools |
[87] | Pathology cracks in masonry and timber architectural heritage | 3D laser scanning data, DL model for crack detection and identification | DT modeling for the detection, classification, and restoration of pathology cracks | Integration of 3D laser scanning with Mask R-CNN for crack area detection and Fully Convolutional Network (FCN) for single crack identification and calculation | 3D laser scanner (Leica BLK360), DL models (Mask R-CNN, FCN), point cloud data processing |
[88] | Indoor air quality impact on historical artifacts | Indoor air quality data from Historical Museum | Microclimatic investigation for conservation | Monitoring of temperature, humidity, and particulate matter using low-cost sensors | Arduino-based sensor system |
[89] | Environmental risks (temperature, humidity, VOC, CO2) affecting cultural heritage and buildings | IoT sensors for monitoring temperature, relative humidity, VOCs, and CO2, integrated with the H-BIM platform | Preventive conservation using real-time environmental monitoring and DT technology | Integration of IoT sensor data with H-BIM for real-time monitoring and management, 3D modeling of building components, analysis of environmental conditions | WSN, H-BIM, IoT devices (Bosch BME 280 sensors, CCS811 for CO2 and VOCs), BIM Server, MongoDB (database management system), Node-RED (web-based editor for IoT data collection and management) |
[24] | Environmental risks (temperature fluctuations, humidity, wet/dry conditions) affecting heritage degradation | IoT sensors from four cultural heritage sites | A project integrating IoT for continuous microclimate monitoring with real-time risk assessment | IoT sensors monitoring temperature, humidity, wet/dry conditions, data transmitted via Narrowband Internet of Things (NB-IoT), edge processing | NB-IoT-enabled smart sensors (Sensirion SHTC3, SAMYOUNG SY-DS-1L), Message Queue Telemetry Transport (MQTT) protocols, Node-RED visualization |
[93] | Degradation risks (humidity, temperature, structural issues and biological growth) | Case studies from heritage buildings with IoT sensor deployment for continuous environmental monitoring | Integration of IoT and H-BIM for smart monitoring, real-time data collection, and resilient preservation management | IoT sensors tracking environmental factors (temperature, humidity, gas, vibration), integrated with H-BIM for predictive maintenance | IoT sensors, Radio Frequency Identification (RFID) for material tracking, AI for hazard detection, H-BIM for visualization and data management |
[97] | Seasonal indoor radon (Rn) variability and exposure risks, particularly during winter months | Long-term monitoring of a heritage building using LoRa-enabled IoT edge device | IoT monitoring for indoor radon and environmental factors, focusing on seasonal variation and IAQ-energy efficiency balance | Continuous data acquisition with Rn, temperature, humidity, and pressure sensors, using Fast Fourier Transform (FFT) analysis for trend detection | LoRa-enabled IoT edge device, RD200M Rn sensor, DHT11 temperature/humidity sensor, MPL3115A2 pressure sensor, FFT analysis |
[102] | Functional deterioration due to climate change | Analysis of 79 heritage timber buildings across five locations | Fuzzy logic model (FBSL2.0) for functional service life prediction under climate change scenarios (RCP2.6 and RCP8.5) | AI-based analysis using fuzzy logic to model the functional deterioration under predicted climate change scenarios (2045–2069) | Xfuzzy 3.0, predictive simulations for climate impact assessment (RCP2.6 and RCP8.5) |
[103] | Challenges in conservation and refurbishment of historic public buildings | Survey data, historic datasets, photographic surveys, laser scans | H-BIM for knowledge sharing and sustainable conservation | ML for object recognition, CFD modeling, parametric modeling of typical building systems (e.g., vaulted ceilings, ventilation systems) | H-BIM, GIS mapping, CFD analysis, 3D laser scanning, DL for object recognition |
[108] | Degradation and anomaly detection in historic buildings | Case study datasets | BIM integrated with DL models for anomaly detection and classification | 3D point cloud data acquisition, supervised DL Convolutional Neural Networks (CNN), and automated classification of building anomalies | Terrestrial Laser Scanner (TLS), CNN (ResNet architecture), Revit plug-in, Cyclone Register 360, Python-based DL models, Binder |
[119] | Preventive maintenance and resilience management of World Heritage Sites | Expert system applying the fuzzy inference system (Art-Risk3.5) for assessing vulnerability and functional service life | Digital management for preventive conservation and resilience of heritage sites | Fuzzy inference system (FIS) to evaluate vulnerability, risks, and service life of heritage buildings | Fuzzy logic algorithms, Art-Risk3.5, digital management tools, IoT devices for monitoring |
[121] | Data sensitivity to lighting and texture conditions | Drone and laser scanning survey of a case study | DL for Historical Masonry Segmentation | Convolutional Neural Networks (CNN) for image segmentation of brickwork | Drone photogrammetry, laser scanning, CNN algorithms |
[123] | Deformation monitoring of cultural heritage structures | Terrestrial laser scanning (TLS), point clouds, 3D model of structures | Validation of deformation measurement techniques for historic buildings | TLS point cloud comparison, Artificial Neural Network (ANN) for deformation prediction, multi-epoch point cloud analysis | TLS (Leica Scan Station 2), ANN, Cloud Compare software, 3D modeling |
[124] | Surface damage (water stains, scaling, color aberration, wide gaps) | On-site photos from a case study | Computer Vision for Surface Damage Detection | ML-based method using YOLOv4 for damage detection | Computer vision, YOLOv4 model |
[125] | Structural cracking and deformation monitoring | Unmanned Aerial Vehicle (UAV) photogrammetry, Structure from Motion (SfM), 3D Digital Twin | DT model for damage-augmented structural health monitoring | UAV photogrammetry, SfM, automatic crack detection using ML, and comparison with manual measurement | UAV (Anafi Parrot), Context Capture software, ML-based crack detection, SfM algorithms |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Laohaviraphap, N.; Waroonkun, T. Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies. Buildings 2024, 14, 3979. https://doi.org/10.3390/buildings14123979
Laohaviraphap N, Waroonkun T. Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies. Buildings. 2024; 14(12):3979. https://doi.org/10.3390/buildings14123979
Chicago/Turabian StyleLaohaviraphap, Neeraparng, and Tanut Waroonkun. 2024. "Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies" Buildings 14, no. 12: 3979. https://doi.org/10.3390/buildings14123979
APA StyleLaohaviraphap, N., & Waroonkun, T. (2024). Integrating Artificial Intelligence and the Internet of Things in Cultural Heritage Preservation: A Systematic Review of Risk Management and Environmental Monitoring Strategies. Buildings, 14(12), 3979. https://doi.org/10.3390/buildings14123979