Generative AI in the Construction Industry: A State-of-the-art Analysis

The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel and realistic data or content, such as text, image, video, or code, based on some input or prior knowledge, offers innovative and disruptive solutions to address these challenges. However, there is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to fill this gap by providing a state-of-the-art analysis of generative AI in construction, with three objectives: (1) to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry; (2) to propose a framework for construction firms to build customized generative AI solutions using their own data, comprising steps such as data collection, dataset curation, training custom large language model (LLM), model evaluation, and deployment; and (3) to demonstrate the framework via a case study of developing a generative model for querying contract documents. The results show that retrieval augmented generation (RAG) improves the baseline LLM by 5.2, 9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study provides academics and construction professionals with a comprehensive analysis and practical framework to guide the adoption of generative AI techniques to enhance productivity, quality, safety, and sustainability across the construction industry.Comment: 74 pages, 11 figures, 20 table

Clustering Methods for Network Data Analysis in Programming

In the modern world, data volumes are constantly increasing, and clustering has become an essential tool for identifying patterns and regularities in large datasets. The relevance of this study is associated with the growing need for effective data analysis methods in programming. The objective is to evaluate different clustering techniques within the programming domain and explore their suitability for analysing a wide range of datasets. Inductive and deductive methodologies, concrete illustrations, and visual techniques were employed. The clustering techniques were implemented using RStudio and Matlab tools. The study's findings facilitated the identification of crucial attributes of clustering techniques, including hierarchical structure, cluster quantity, and similarity metrics. The application of several data analysis and visualisation approaches, including k-means, c-means, hierarchical, least spanning tree, and linked component extraction, was illustrated. This study elucidated the clustering approaches that may be optimally employed in various contexts, resulting in enhanced precision in analyses and data-informed decision-making. The study's practical significance is in enhancing programmers' methodological toolset with tools for data analysis and processing

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Exploration of Building Information Modeling and Integrated Project Cloud Service in early architectural design stages

[EN] In the evolving Architecture, Engineering, and Construction (AEC) industry, the use of Building Information Modeling (BIM) and Integrated Project Cloud Service (IPCS) has become crucial. These tools are particularly essential during the early design stages, as they enable comprehensive management and integration of project information, thus promoting effective decision-making throughout project lifecycles. This combined approach enhances inter-organizational collaborations, improves design and construction practices, and creates a communal data platform for stakeholders. This research explores the effectiveness of the BIM-IPCS system in streamlining data exchange and information flow during early design, suggesting ways to minimize errors, speed up processes, and reduce construction costs through dependable networks. Conclusively, this study underscores the significant impact of the BIM-IPCS system on project management, ensuring well-coordinated and informed construction while advocating for its role in driving innovative and efficient project delivery in the AEC industry.Grateful acknowledgment is extended to the National Taiwan University of Science and Technology, the Public Works Information Institute of the Republic of China (CPWEIA), and Luxor Digital Co., Ltd. (LUXOR) for their substantial support and contributions to this research.Wagiri, F.; Shih, S.; Harsono, K.; Cheng, T.; Lu, M. (2023). Exploration of Building Information Modeling and Integrated Project Cloud Service in early architectural design stages. VITRUVIO - International Journal of Architectural Technology and Sustainability. 8(2):26-37. https://doi.org/10.4995/vitruvio-ijats.2023.2045326378

Faculty of Computer Science

Information about the Faculty of Computer Science of the Technische Universität Dresden, data and facts and a selection of current research projects, 2009Informationen über die Fakultät Informatik der TU Dresden, Daten und Fakten sowie eine Auswahl aktueller Forschungsprojekte, 200

Data-Driven Models, Techniques, and Design Principles for Combatting Healthcare Fraud

In the U.S., approximately $700 billion of the$2.7 trillion spent on healthcare is linked to fraud, waste, and abuse. This presents a significant challenge for healthcare payers as they navigate fraudulent activities from dishonest practitioners, sophisticated criminal networks, and even well-intentioned providers who inadvertently submit incorrect billing for legitimate services. This thesis adopts Hevner’s research methodology to guide the creation, assessment, and refinement of a healthcare fraud detection framework and recommended design principles for fraud detection. The thesis provides the following significant contributions to the field:1. A formal literature review of the field of fraud detection in Medicaid. Chapters 3 and 4 provide formal reviews of the available literature on healthcare fraud. Chapter 3 focuses on defining the types of fraud found in healthcare. Chapter 4 reviews fraud detection techniques in literature across healthcare and other industries. Chapter 5 focuses on literature covering fraud detection methodologies utilized explicitly in healthcare.2. A multidimensional data model and analysis techniques for fraud detection in healthcare. Chapter 5 applies Hevner et al. to help develop a framework for fraud detection in Medicaid that provides specific data models and techniques to identify the most prevalent fraud schemes. A multidimensional schema based on Medicaid data and a set of multidimensional models and techniques to detect fraud are presented. These artifacts are evaluated through functional testing against known fraud schemes. This chapter contributes a set of multidimensional data models and analysis techniques that can be used to detect the most prevalent known fraud types.3. A framework for deploying outlier-based fraud detection methods in healthcare. Chapter 6 proposes and evaluates methods for applying outlier detection to healthcare fraud based on literature review, comparative research, direct application on healthcare claims data, and known fraudulent cases. A method for outlier-based fraud detection is presented and evaluated using Medicaid dental claims, providers, and patients.4. Design principles for fraud detection in complex systems. Based on literature and applied research in Medicaid healthcare fraud detection, Chapter 7 offers generalized design principles for fraud detection in similar complex, multi-stakeholder systems.<br/

Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

Consistency Algorithms and Protocols for Distributed Interactive Applications

The Internet has a major impact not only on how people retrieve information but also on how they communicate. Distributed interactive applications support the communication and collaboration of people through the sharing and manipulation of rich multimedia content via the Internet. Aside from shared text editors, meeting support systems, and distributed virtual environments, shared whiteboards are a prominent example of distributed interactive applications. They allow the presentation and joint editing of documents in video conferencing scenarios. The design of such a shared whiteboard application, the multimedia lecture board (mlb), is a main contribution of this thesis. Like many other distributed interactive applications, the mlb has a replicated architecture where each user runs an instance of the application. This has the distinct advantage that the application can be deployed in a lightweight fashion, without relying on a supporting server infrastructure. But at the same time, this peer-to-peer architecture raises a number of challenging problems: First, application data needs to be distributed among all instances. For this purpose, we present the network protocol RTP/I for the standardized communication of distributed interactive applications, and a novel application-level multicast protocol that realizes efficient group communication while taking application-level knowledge into account. Second, consistency control mechanisms are required to keep the replicated application data synchronized. We present the consistency control algorithms “local lag”, “Timewarp”, and “state request”, show how they can be combined, and discuss how to provide visual feedback so that the session members are able to handle conflicting actions. Finally, late-joining participants need to be initialized with the current application state before they are able to participate in a collaborative session. We propose a novel late-join algorithm, which is both flexible and scalable. All algorithms and protocols presented in this dissertation solve the aforementioned problems in a generic way. We demonstrate how they can be employed for the mlb as well as for other distributed interactive applications

Visualizing Large Business Process Models: Challenges, Techniques, Applications

Large process models may comprise hundreds or thousands of process elements, like activities, gateways, and data objects. Presenting such process models to users and enabling them to interact with these models constitute crucial tasks of any process-aware information systems (PAISs). Existing PAISs, however, neither provide adequate techniques for visualizing and abstracting process models nor for interacting with them. In particular, PAISs do not provide tailored process visualizations as needed in complex application environments. This paper presents examples of large process models and discusses some of the challenges to be tackled when visualizing and abstracting respective models. Further, it presents a comprehensive framework that allows for personalized process model visualizations, which can be tailored to the specific needs of the different user groups. First, process model complexity can be reduced by abstracting the models, i.e., by eliminating or aggregating process elements not relevant in the given visualization context. Second, the appearance of process elements can be customized independent of the process modeling language used. Third, different visualization formats (e.g., process diagrams, process forms, and process trees) are supported. Finally, it will be discussed how tailored visualizations of process models may serve as basis for changing and evolving process models at a high level of abstraction

Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview

In recent years, the rapid advancement and impressive capabilities of Large Language Models (LLMs) have been evident across various domains. This paper explores the application, implications, and potential of LLMs in building energy efficiency and decarbonization studies. The wide-ranging capabilities of LLMs are examined in the context of the building energy field, including intelligent control systems, code generation, data infrastructure, knowledge extraction, and education. Despite the promising potential of LLMs, challenges including complex and expensive computation, data privacy, security and copyright, complexity in fine-tuned LLMs, and self-consistency are discussed. The paper concludes with a call for future research focused on the enhancement of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research between AI and energy experts