Smart city construction is essential for sustainable urban development, particularly in developing countries facing urbanization and resource constraints. Current research is limited by cross-sectional and qualitative approaches. This study introduces a dynamic evaluation framework using a longitudinal case study of Shanghai (2012-2021), applying the entropy weight method to assess nine indicators across technology, economy, and environment. Results show that technological R&D, science and technology expenditure, foreign direct investment, and high-tech industrial output are more crucial contributors. The study offers a quantitative, longitudinal approach to smart city evaluation, providing a practical reference for policymakers in emerging cities of developing countries.
Automated Compliance Checking (ACC) facilitates rapid and objective review of building permits. The present study, based on in-depth qualitative expert interviews with regulators and ACC experts worldwide, provides an overview of the current ACC advances in place within or linked to regulatory bodies. The interviewees highlighted key challenges, including ambiguous terminology, inconsistencies, and the balance between human oversight and algorithmic decision-making. A comparative analysis of current practices across different countries offers insights into their lessons learned, future plans, and additional research needs.
Safety behavior of contractors is influenced by the presence of a national insurance database shared among insurance companies to adapt insurance premiums to the contractor’s safety records. In many developing countries, such a national database does not exist which may hinder safety performance. This study develops an agent based simulation framework that studies the impact of a national insurance database on a contractor’s safety performance. Results showed that the implementation of the database led to an overall increase in the average safety awareness within the market and created a stronger incentive for contractors to update their safety practices.
This study explores blockchain's potential in real estate development, focusing on the economic, legal, and technical framework for tokenization. Real estate development is a high-risk, capital-intensive process with long timelines. Blockchain can mitigate these challenges by dividing ownership and financial flows into digital tokens. Based on a literature review, the research examines the conditions for tokenization, suitable token structures, and its impact on project conception, considering different asset classes and property uses. Expert interviews are conducted to examine the necessary technical, legal and economic conditions for the implementation of tokenization in practice, additionally identifying both potential benefits and challenges.
Information delivery specification (IDS), model view definition (mvdXML) and Gherkin are the main encodings of quality assurance rules for Industry Foundation Classes (IFC) models governed and/or employed by buildingSMART International (bSI). Our comparison shows that each of them addresses the serialization of exchange information requirement (EIR) rules in its own specific way. IDS provides fastest time-to-market with its limited vocabulary, Gherkin rules support encoding of the most requirements with most effort, and mvdXML provides the best trade-off between broad support for various requirements while retaining the benefits of a limited vocabulary for fast implementation.
The architecture, engineering, and construction (AEC) industry still heavily relies on information stored in drawings. However, information extraction (IE) from building drawings is time-consuming and costly, especially when dealing with historical buildings. Drawing search can be simplified by leveraging the information stored in the title block. The work proposes an proposes a novel title block detection and IE pipeline by integrating Faster R-CNN and GPT-4o which enables advanced search and outperforms existing methods. An extensible domain-expert annotated dataset is produced via a novel AEC-friendly annotation pipeline that lays the foundation for future work.
Despite the recognised benefits of BIM and Building Performance Simulation (BPS) in managing built heritage, developing and adopting them remains a resource-intensive task. Interoperability issues make it difficult to effectively use BIM data for performance assessment in building management, limiting the possibilities of combining, integrating and using building performance data to create knowledge-based tools supporting decision-making. To address the BIM-BPS interoperability issue, this paper introduces a semi-automated approach for developing space-oriented, BPS-compatible BIMs. Applied to a university building, the presented method generates semantically rich models for integrated, occupancy-based energy and fire safety analyses, demonstrating scalability for large stocks.
Balancing energy efficiency and occupant comfort is key to maintaining the sustainability of buildings. Understanding occupant activities is essential for optimising energy use without compromising comfort. This paper proposes a self-supervised learning approach for recognising occupant activity patterns using indoor environmental data from the Building Management System (BMS). A modified Transformer Masked Autoencoder (Ti-MAE) is adopted to extract latent representations of data, followed by the K-means Clustering Algorithm for clustering typical occupant activity patterns. Experiments using real-life building data demonstrate its robust performance in occupant activity recognition, even without specific sensors. The approach optimises energy efficiency while preserving privacy.
Cost and time must be continuously balanced for construction project success based on resource utilization. However, resources are usually planned implicitly and not aligned between scheduling and cost estimation, leading to inconsistencies in project management processes. To address this issue, this paper proposes a resource-centric approach that integrates building geometry with schedule and cost data using ontologies as the semantic foundation. A web prototype converts and links the original data into an RDF graph, enabling advanced SPARQL queries for cross-domain consistency checks, particularly on resource usage.These checks validate the alignment of interconnected project domains, providing insights unattainable with traditional methods.
