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The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

This paper provides a critical review of developments in the adaptability of buildings. The purpose of this paper is to determine the current “state-of-the-art”, describe current thinking and trends in research and practice, and identify issues and gaps that further research can address. It provides a basis for a scientific and practical understanding of the interdependencies across different design criterion. This paper increases the awareness of architects, engineers, clients and users on the importance of adaptability and its role in lowering impacts over the lifecycle of buildings as part of the infrastructure system.

This paper draws mainly from the literature as its source of evidence. These were identified from established databases and search engines (e.g. Scopus, ISI Web of Knowledge and Google Scholar) using keywords such as adaptability, adaptable, adaptation, and flexibility. Over 80 sources including books, journal papers, conference proceedings, research reports and doctoral theses covering the period 1990 to 2017 were reviewed and categorised. An inductive approach was used to critically review and categorise these publications and develop a framework for analysis.

The concept of adaptability includes many dimensions which can broadly fall into two categories: changes to buildings and user adaptations to buildings. However, previous research has mostly focussed on the former, with many attempts to identify building attributes that facilitate adaptability, and some considerations for its assessment. Key areas that have not been adequately addressed and which require further research include: user/occupant adaptations, cost, benefits and implications of various adaptability measures, and the development of a standardised assessment methodology that could aid in decision making in the design stage of buildings.

The adaptability strategies considered in this review focussed mainly on building components and systems, and did not include the contribution of intelligent and smart/biological systems. The coverage is further limited in scope due to the period considered (1990-2017) and the exclusion of terms such as “retrofit” and “refurbishment” from the review. However, the findings provide a solid basis for further research in the areas identified above. It identifies research issues and gaps in knowledge between the defined needs and current state-of-the-art on adaptive building for both research and practice.

This paper is a review of research into a highly topical subject, given the acknowledged need to adapt buildings over their lifecycle to environmental, economic or social changes. It provides further insights on the dimensions of adaptability and identifies areas for further research that will contribute to the development of robust tools for the assessment of building adaptability, which will enhance the decision-making process of building design and the development of a more sustainable built environment.