Information Model Research Articles

Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing

In the era of Industry 4.0, Zero Defect Manufacturing (ZDM) has emerged as a prominent strategy for quality improvement, emphasizing data-driven approaches for defect prediction, prevention, and mitigation. The success of ZDM heavily depends on the availability and quality of data typically collected from diverse and heterogeneous sources during production and quality control, presenting challenges in data interoperability. Addressing this, we introduce a novel approach leveraging Asset Administration Shell (AAS) and Large Language Models (LLMs) for creating interoperable information models that incorporate semantic contextual information to enhance the interoperability of data integration in the quality control process. AAS, initiated by German industry stakeholders, shows a significant advancement in information modeling, blending ontology and digital twin concepts for the virtual representation of assets. In this work, we develop a systematic, use-case-driven methodology for AAS-based information modeling. This methodology guides the design and implementation of AAS models, ensuring model properties are presented in a unified structure and reference external standardized vocabularies to maintain consistency across different systems. To automate this referencing process, we propose a novel LLM-based algorithm to semantically search model properties within a standardized vocabulary repository. This algorithm significantly reduces manual intervention in model development. A case study in the injection molding domain demonstrates the practical application of our approach, showcasing the integration and linking of product quality and machine process data with the help of the developed AAS models. Statistical evaluation of our LLM-based semantic search algorithm confirms its efficacy in enhancing data interoperability. This methodology offers a scalable and adaptable solution for various industrial use cases, promoting widespread data interoperability in the context of Industry 4.0.

The genetic code and its [formula omitted]-adic ultrametric modeling

The genetic code plays a central role in all living organisms and its modeling is important for describing and understanding involved the coding rules. There are many approaches to modeling various aspects of the genetic code. One of the simple and successful mathematical tools for modeling the similarity both between codons and between amino acids, is the ultrametrics and especially the p-adic distance. This article contains an overview of ultrametric (p-adic) modeling of genetic information, and its translation to proteins using the genetic code.

Trust Number: rust-based modeling for handling decision-making problems

Fuzzy sets play an effective role in dealing with the uncertainty and ambiguity of input data in real-world decision-making problems. Nevertheless, the effectiveness of fuzzy sets becomes unreliable and even more uncertain when the input data come from untrustworthy sources. Therefore, a new measurement could be considered based on the data's degree of trust to reduce the deviation of unreliable information in fuzzy decision-making problems. The main aim of this study is to introduce a new information modeling called trust numbers (T-numbers), which models variations and deviations associated with triangular fuzzy numbers and their application to decision-making. In addition, it introduces new operations on T-numbers to develop a decision model based on this theory. The performance of this model was analyzed through its implementation in two case studies and by comparing the fuzzy technique for order of Preference by similarity to the ideal solution (F-TOPSIS) and its T-number extension(T-TOPSIS). Results indicate that T-numbers can be applied to classical fuzzy numbers when the available information is uncertain and a degree of distrust exists.

Sonar combines deep learning and building information modeling for underwater crack detection of concrete structures

Sonar combines deep learning and building information modeling for underwater crack detection of concrete structures

Heritage Building Information Modelling (HBIM) for Heritage Conservation: Framework of Challenges, Gaps, and Existing limitations of HBIM

This study investigates the pivotal role of Heritage Building Information Modelling (HBIM) in conservation of cultural heritage buildings. Utilizing a systematic literature review and in-depth analysis of 59 studies within 2013-23 period, the study underscores HBIM's capacity to document, analyze, and manage heritage structures. It delineates the workflow from data acquisition through laser scanning and photogrammetry, to the modeling of historic fabric, highlighting the integration of digital technologies in conservation practices. The paper identifies key challenges in HBIM adoption, including technical limitations, the need for specialized skills, and the complexity of accurately modeling historical details. It proposes avenues for future research focused on enhancing data acquisition techniques, improving the interoperability of HBIM with other digital tools, and developing standards for the effective documentation of heritage sites. The findings advocate for a collaborative approach, leveraging HBIM to foster interdisciplinary partnerships between architects, historians, and conservationists, for sustainable preservation of cultural heritage.

Optimization of building thermal environment in industrial heritage landscape regeneration design simulation based on image visual visualization

With the increasing attention paid to the regeneration design of industrial heritage landscape, how to effectively improve the thermal energy environment in these areas has become a research hotspot. The thermal efficiency of buildings not only affects the energy consumption, but also relates to the comfort of users and the sustainable development of the region. This study aims to explore the optimization method of building thermal energy environment based on image visual visualization, so as to achieve efficient regeneration of industrial heritage landscape, and improve the thermal energy performance and environmental adaptability of buildings through scientific design and simulation means. Through the use of computer aided design (CAD) and building information modeling (BIM) techniques, combined with heat flow simulation and visual image analysis, a detailed thermal environment assessment is carried out for selected industrial heritage buildings. Through data collection and analysis, we take into account factors such as sunlight, ventilation and heat loss in the building to develop an optimization plan. The simulation results show that the energy consumption of buildings with optimized thermal environment design is significantly reduced and the indoor thermal comfort is greatly improved. Visual tools effectively help the design team identify problem areas and provide intuitive data support for all parties during the design process. The optimization method of building thermal energy environment based on image visual visualization provides a new idea for the regeneration design of industrial heritage landscape, which helps to realize the dual goals of protecting the historical value and meeting the needs of modern environmental protection.

BIM framework for efficient material procurement planning

Inefficient procurement processes can lead to increased costs and project delays. Addressing information management inefficiencies is a significant but largely unexplored area within construction procurement strategies, despite potential for automation through Database Management Systems (DBMS) and Industry Foundation Classes (IFC). Subjective approaches constrain procurement planning, hindering optimal solutions. This paper addresses the gap by developing a comprehensive semi-automated procurement planning framework. The framework offers flexibility through a two-phased optimization employing Particle Swarm Optimization (PSO) or Genetic Algorithm (GA), integrated with a Building Information Modeling (BIM)-driven database platform compatible with various modeling software. It enhances decision-making by considering indirect costs and allowing installment payments while generating a 4D schedule for improved supply chain stakeholder visualization and decision-making (e.g., project managers), demonstrating improvements over traditional procurement plans in a real-world case study. The developed framework enables future research on integrating real-time data, predictive analytics, and smart contracts to further enhance procurement management.

Optimizing ventilation systems considering operational and embodied emissions with life cycle based method

Efforts to enhance the energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems have been bolstered by technical advancements and stringent regulations. However, HVAC systems not only emit during operation due to energy consumption but also have significant embodied emissions, which recent studies show can exceed those of the operational phase. This study introduces an optimization framework aimed at minimizing lifetime emissions—both operational and embodied—for ventilation systems, an area previously underexplored. The optimization framework incorporates detailed calculations of pressure drop, fan power and newly developed life cycle ventilation inventory data with a life cycle assessment perspectiveA case study of ventilation ductwork in a “BREEAM Excellent” certified energy-efficient building in Norway applies this optimization framework. Findings indicate that for this case, optimizing ductwork dimensions can reduce lifetime emissions of the ventilation system by 15 %, compared to the existing designs with an emission intensity of 0.3 kg CO2/kWh. Further, the study examines how the emission intensity of electricity generation and service lifetime influence total emissions, highlighting the growing importance of embodied emissions as electricity generation becomes cleaner. This underscores the necessity of considering both operational and embodied emissions in design decisions. This study presents an optimization tool for low-emissions ventilation design, capable of processing diverse layouts and aiding in decarbonizing ventilation systems towards achieving zero-emission buildings. Future integration with building information modeling (BIM) and artificial intelligence (AI) could further enhance autonomous low-carbon design and decision-making.

Framework to assess connection of risk factors and management strategies in Building Information Modeling

Framework to assess connection of risk factors and management strategies in Building Information Modeling

Enhancing Building Information Modeling Effectiveness Through Coopetition and the Industrial Internet of Things

The construction industry plays a crucial role in the global economy but faces significant challenges, including inefficiencies, high costs, and environmental impacts. Although Building Information Modeling (BIM) has been widely adopted as a solution to these issues, its practical impact remains limited. This study investigates how manufacturers can enhance their contributions to improve BIM’s effectiveness, proposing that coopetition practices—combining competition and cooperation—can positively influence these contributions, thereby enhancing the benefits of BIM. To explore this hypothesis, an Experimental Coopetition Network was implemented in the Portuguese ornamental stone (POS) sector, utilizing Industrial IoT technology to facilitate collaboration among selected companies. The study assessed the impact of coopetition practices on key performance indicators related to BIM, including on-time delivery, labor productivity, and CO2 emissions. The findings demonstrate significant improvements in scheduling, operational efficiency, and environmental sustainability, validating the hypothesis that coopetition practices can enhance manufacturers’ contributions to BIM. These results suggest that coopetition practices contribute to better project outcomes, increased competitiveness, and sustainability within the construction industry. Despite the promising results, the study acknowledges limitations such as the scope of the sample size and observation periods, indicating areas for future research. This research contributes to the theoretical framework of coopetition, aligning with the United Nations Sustainable Development Goals (SDGs), and provides valuable insights for industry practitioners and policymakers seeking to implement more sustainable construction practices.

SCOPE ON BIM AS A TOOL FOR DESIGNING ARCHITECTURAL PROJECTS FOR ARCHITECTS AND ENGINEERS. CASE OF ALGERIA

Building Information Modeling, often known as BIM, is considered an indispensable component of the design and construction process in many nations throughout the world. In order to analyze the practitioners' perceptions of BIM's influence on construction in connection to the many tools and software, surveys were carried out and analyzed with the sphinx tool. The surveys were also carried out to determine the architects' and engineers' capacities to integrate BIM into their design processes. Despite having limited understanding about BIM as a new method of work, the results indicated that both architects and engineers are concerned about improving design processes in order to provide a higher level of quality to the projects they are working on in Algerian context.

Exploring counselor experiences of training in the PR6 resilience model

AbstractResilience‐related knowledge and skills are part of all counselors‐in‐training foundational curriculum. We used interpretative phenomenological analysis to explore nine novice counselors’ perceived training experiences in the Predictive 6 Factor (PR6) model of resilience. We identified the following group experiential themes: value of the neuroscience information and PR6 model, counselors’ personal resonance and buy‐in, and considerations for practice. We also identified nine subthemes representing more nuanced elements of the larger themes.

Assessment of building information modeling adoption in building material scheduling in Tanzania’s construction industry

The building construction industry continues to face challenges that affect project delivery, one of which is the traditional practices used for building material scheduling (BMS). Studies indicate that these practices are often inefficient and prone to errors, leading to delays, cost overruns, and low-quality work, affecting the project objectives. Building Information Modeling (BIM) presents a promising solution for addressing these challenges, proven through existing integrations. However, its adoption in BMS practices in construction is still relatively low. Therefore, this study aimed to assess BIM use in BMS in Tanzania’s building construction industry. The study adopted a quantitative research approach, and the study population of 153 registered quantity surveying firms was purposively selected. The study data was collected using online questionnaires and analyzed with the Statistical Package for Social Sciences (SPSS). The study findings indicate that traditional practices are still favored over BIM when scheduling building materials in Tanzania, with the most predominant practices being spreadsheets and paper-based methods. Additionally, findings suggest that BIM is not a new concept in Tanzania. However, its adoption in the current practices is relatively low, mainly due to insufficient expertise, training resources, and limited access to BIM software/tools. Moreover, findings indicate a significant belief that BIM can improve BMS practices. These insights can inform policymakers, industry stakeholders, and educational institutions on the necessary steps to promote BIM integration. Therefore, it is recommended that BIM adoption be prioritized by addressing these barriers to enhance project outcomes. Shifting from traditional methods to BIM will lead to more efficient and effective construction processes, making BIM's broader adoption essential for the industry's future success.

Building Information Modeling Adoption in the Palestinian Construction Industry: Influencing Factors, External Determinants, and Technology Acceptance

ABSTRACT Building Information Modeling (BIM) is a worldwide spreading technology for the Architecture, Engineering, and Construction (AEC) industry that allows for continuous modification and improvement of building models, resulting in improved quality and cost savings in the project. The study aims to investigate the adoption pattern of BIM in the Palestinian market and identify significant factors that influence the adoption process, including top management support, organizational culture, and the number of experts and technicians. A structured questionnaire was developed, distributed electronically, and 168 valid responses were received, leading to a margin error of 4.4% and a response rate of 67.2%. A model, based on the Technology Acceptance Model (TAM) was developed and partial least squares structural equation modeling (PLS-SEM) was utilized to evaluate the measurement model’s reliability and validity, test hypotheses regarding the relationships between constructs, and assess the impact of various factors on the intention to adopt BIM within the construction industry. Six external influences on acceptance are highlighted. Individual and organizational acceptance drivers are identified, including top management support, ease of use, and competitive landscape. Government policies impact adoption and results stress internal and external considerations for BIM adoption.

Mixed Reality-Based Inspection Method for Underground Water Supply Network with Multi-Source Information Integration

Regular on-site inspection is crucial for promptly detecting faults in water supply networks (WSNs) and auxiliary facilities, significantly reducing leakage risks. However, the fragmentation of information and the separation between virtual and physical networks pose challenges, increasing the cognitive load on inspectors. Furthermore, due to the lack of real-time computation in current research, the effectiveness in detecting anomalies, such as leaks, is limited, hindering its ability to provide immediate and direct-decision support for inspectors. To address these issues, this research proposes a mixed reality (MR) inspection method that integrates multi-source information, combining building information modeling (BIM), Internet of Things (IoT), monitoring data, and numerical simulation technologies. This approach aims to achieve in situ visualization and real-time computational capabilities. The effectiveness of the proposed method is demonstrated through case studies, with user feedback confirming its feasibility. The results indicate improvements in inspection task performance, work efficiency, and standardization compared to traditional mobile terminal-based methods.

Principles for Sustainable Integration of BIM and Digital Twin Technologies in Industrial Infrastructure

As industries evolve towards greater digitalization, integrating Building Information Modeling (BIM) and digital twin technologies presents a unique opportunity to enhance sustainability in industrial infrastructure. This paper formulates a comprehensive set of principles aimed at guiding the sustainable integration of these technologies within the context of modern industrial facilities, often referred to as “Factories of the Future”. The principles are designed to address critical sustainability challenges, including minimizing environmental impact, optimizing resource efficiency, and ensuring long-term resilience. Through a detailed examination of lifecycle management, data interoperability, and collaborative stakeholder engagement, this work provides a strategic framework for leveraging digital technologies to achieve sustainability goals. The principles outlined in this paper not only promote greener industrial practices but also pave the way for innovation in the sustainable development of industrial infrastructure. This framework is intended to serve as a foundation for future research and practical application, supporting the global shift towards more sustainable industrial operations.

Developing a theoretical framework for enhancing green project approaches via Agile methodology.

The application of green building approaches has significantly increased in many countries and buildings, accompanied by the development of numerous standards and methodologies for their implementation. Despite these efforts, not all risks and challenges facing green projects have been eliminated in some regions. Issues such as financing difficulties, budget overruns, and schedule delays continue to pose significant challenges. As a result, no substantial improvements have been made in the approach to green projects. Therefore, this study aims to develop a theoretical framework that enhances the approach to green projects. By utilizing the AGILE methodology, specifically SCRUM, the most significant risks facing green projects were identified and converted into indicators based on prior studies. Furthermore, Building Information Modeling (BIM) technology, particularly Green BIM (GBIM), was employed to create tools and elements to address these challenges, leading to the development of a conceptual framework capable of providing solutions. This framework was built around 30 indicators, whose weights were determined through an electronic questionnaire involving 140 specialists in green projects, AGILE methodology, and BIM technology. Assistance from academic experts was also sought. The results of the questionnaire, analyzed using statistical methods, contributed to the development of a theoretical framework aimed at improving the approach to green projects.

Improved Building Information Modeling Based Method for Prioritizing Clash Detection in the Building Construction Design Phase

The rising complexity of construction projects and the industry’s commitment to sustainable practices have driven the extensive adoption of Building Information Modeling (BIM) technology. A core function of BIM is the early identification and resolution of clashes during the design phase, which serves to mitigate costly rework and delays in the construction process. This study presents an advanced method for classifying and prioritizing hard clashes between structural components and mechanical, electrical, and plumbing (MEP) systems. Employing the Best-Worst Method (BWM), this research assigned specific weights to structural and MEP elements based on expert evaluations. Six parameters were incorporated into this prioritization framework: the weights determined by the BWM, outputs from Navisworks software (v2021), the ratio of MEP volume to floor volume, the functional purpose of each floor, and the number of adjacent elements. A custom-developed plugin for Autodesk Navisworks integrated these parameters, enabling real-time automated clash prioritization. Clashes were ranked by criticality through a calculation involving the six parameters, which enhanced the efficiency of clash detection by optimizing time and cost considerations during the design phase. Case study results indicate that beams and columns represent the most critical structural elements, while ducts are identified as the most significant MEP elements. The proposed method substantially improves clash detection and prioritization efficiency and accuracy, yielding considerable benefits in project management and resource allocation.

Digital Twins in the Sustainable Construction Industry

Digital Twin (DT) technology, as the evolution of Building Information Modeling (BIM), has emerged to address global concerns regarding the environmental impacts of the construction industry and to meet sustainability indicators. Despite numerous studies targeting the integration of DT and sustainability, there is a noticeable gap in creating a comprehensive overview of the efforts and future directions in this field. Therefore, this research aims to provide both a scientometric analysis and a thematic review of 235 papers extracted from the Scopus database. These papers, all published between 2017 and 2024, focus on previous efforts, current trends, and future directions of using the Digital Twin for construction sustainability. In addition, 34 papers that were cited more than 20 times were classified by the application into four categories: simulation, technology integration, smart systems, and literature review. Furthermore, regarding the application of smart systems in sustainability, the authors discussed applications of BIM-DT in smart construction, smart buildings, smart infrastructures, and smart cities based on the most-cited papers. Subsequently, five research gaps were identified and suggested for future investigation. The research gives a holistic insight into the current trend of DT among researchers, previous achievements, and future directions.

Investigating the adoption of building information modeling of small-medium sized organizations in the construction industry: from a configuration perspective

ABSTRACT Building Information Modeling (BIM) is considered to be one of the most promising innovations in the construction industry, and the building industry is beginning to use and benefit from it. However, the full adoption of BIM in the construction industry is still challenging as small and medium-sized organizations (SMOs) are lagging in BIM adoption. Although existing studies have explored many influencing factors of BIM adoption, most of them are focused on a single relationship and cannot explain why certain factor that works for one organization fails in others. This study aims to respond to the research gap in previous studies on BIM adoption in SMOs by highlighting the importance of the combination of influencing factors rather than single correlations, thereby clarifying the realization mechanisms of BIM adoption in SMOs. Using the qualitative comparative analysis (QCA) approach and fsQCA3.0, this study connects the factors from technological, organizational, and environmental aspects under the technology-organization-environment (TOE) framework, five configurations leading to the high BIM adoption and two configurations leading to the low BIM adoption are identified with 36 SMO cases through the configurational lens. As one of the earliest attempts to investigate SMOs’ BIM adoption decisions using the QCA method, this study expects to offer a holistic understanding of BIM adoption in SMOs and contribute to the full BIM diffusion in the construction industry by providing valuable suggestions to industry practitioners. Highlights BIM adoption in SMOs results from a combination of technological, organizational, and environmental conditions, and no necessary conditions lead to BIM adoption behavior. More than one configuration is leading to high or low BIM adoption in SMOs. Under specific contexts, BIM capability can substitute for financial capability, while financial capability will substitute for government pressure. When conditions are missing on both the technological and organizational aspects, government or market pressure alone cannot lead to BIM adoption behavior in SMOs.