Publications

This chapter explores the synopsis of the Industry 5.0 paradigm, focusing on Human-robot collaboration, encompassing critical elements from following the progression of evolution from Industry 4.0 to Industry 5.0 to the implementation of cutting-edge technologies and human-centric approaches within this framework. Industry 5.0 paradigm shift builds upon the foundation laid by Industry 4.0, with a renewed focus on integrating human intelligence and creativity with the capabilities of robots. The Operator of Industry 5.0 embodies the idea of skilled human operators working alongside automated systems to optimize performance and efficiency. Industry 5.0 technologies encompass collaborative robots (cobots) and advancements in robot learning, enabling safe and efficient collaboration between humans and machines and facilitating dynamic partnerships in shared workspaces. Human-centric approaches within Industry 5.0 technologies ensure that technological advancements align with human needs and preferences, fostering a work environment where humans and robots collaborate harmoniously. The concept of the Human Digital Twin offers a compelling instrument for identifying and optimizing human behavior within the context of Industry 5.0, enabling organizations to tailor processes and workflows to individual capabilities and preferences.

here is value in managing organizational knowledge. This knowledge originates from the experiences of organizations and is retained in the form of corporate memory. This retained knowledge is usable in expediting similar work in the future, thus benefıting the organization by assisting in rapid implementation on projects. The main objective of this study is to introduce a Model of Framework for Lessons Learned Process (LLP) that facilitates sharing of knowledge about experiences and transferring of lessons learned at organizational level. The research focus is to 1) explore the processes of knowledge transfer and learning, 2) analyze the use of project feedback, and 3) assess the effectiveness of LLP Model in organizations. The research includes an extensive literature study, interviews with managers on how to manage lessons learned processes throughout the organizations, analysis of this information to develop fındings, and extending these to model an evolving framework of LLP over time in organizations. Without managing lessons learned process, the proper culture, and effıcient technology application, the value of the knowledge, even if it is present, will be signifıcantly less. The managing process adds value to the knowledge by making appropriate knowledge available whenever and wherever it is needed within the organization.

Compression toughness tests were carried out on concrete cylinders reinforced with three different aspect ratios of hooked-end steel fibers 60, 75, and 83 and six different percentages of steel fibers 0.5, 1.0, 1.25, 1.5, 1.75, and 2.0% by volume of concrete. The w/c ratio used for the normal strength steel fiber reinforced concrete mixes (NSSFRC) was 0.55, and the water-cementitious ratio (w/c+s) for the high strength fiber reinforced concrete mixes (HSSFRC) was 0.31. For each mix, three test cylinders were tested for compression specific toughness. The effect of fiber reinforcement index: volume of fibers × length/diameter ratio on compression specific toughness and also on the relationship between these two properties is presented in this paper. As a result, (a) equations are proposed to quantify the effect of fibers on compression toughness ratio of concrete in terms of FRI, (b) equations obtained in terms of FRI and compression specific toughness of plain concrete to estimate both compression specific toughness of NSSFRC and HSSFRC (N.m), (c) equations obtained which represent the relationship between compression toughness index and FRI for NSSFRC and HSSFRC, respectively, and (d) equations obtained to quantify the relationship between compression specific toughness index and fiber reinforcement index for NSSFRC and HSSFRC, respectively. The proposed equations give good correlation with the experimental values.

PurposeThe purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).Design/methodology/approachIDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.FindingsThe research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.Practical implicationsDRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.Originality/valueThe research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

Cyber-physical systems (CPS) comprising interconnected and integrated smart systems can transform the Architecture Engineering Construction (AEC) industry and contribute to the development of Construction 4.0. Digital Twin (DT) construction is a new mode for managing production in construction that leverages the data streaming from a variety of site monitoring technologies and artificially intelligent functions to provide accurate status information and to proactively analyze and optimize ongoing design, planning, and production. DT integrates historical and real-time data obtained from physical systems with physics-based models and advanced analytics to create digital counterparts with high integrity, awareness, and adaptability to provide predictive services to planning and construction entities.

Changes at different phases of a construction project are inevitable due to a multitude of reasons such as design changes, design errors, additions to scope, or unknown conditions that may arise due to resource limitations and the uniqueness of the project. For each change, contractors are entitled to an equitable adjustment to the base contract price and schedule. It is commonly accepted that change orders can have adverse effects on project performance, but these effects are difficult to quantify and manage, and they frequently lead to disputes. Most work in change order management focuses on labor productivity and does not pay as much attention to the quantification and management of the impact of change orders in regard to overall project performance in terms of time and cost. This paper describes a prototype expert system named QUICOPP that implements these ideas. The knowledge used in the system was acquired through a questionnaire survey administered to the contractors in North Cyprus construction industry. A list of factors that describe the adverse effects of change orders on project performance have been identified based on the survey, and this list of factors was used to develop a quantitative model of how different change orders affect the time and cost of a project. Our system provides a cost-effective means for handling change orders through all phases of a project such that construction operations can continue with the least amount of interruption that usually results from of disputes between different parties involved in a project.

This study takes a unique approach by investigating the integration of Brain–Computer Interfaces (BCIs) and Building Information Modeling (BIM) within residential architecture. It explores their combined potential to foster neuro-responsive, sustainable environments within the framework of Construction 5.0. The methodological approach involves real-time BCI data and subjective evaluations of occupants’ experiences to elucidate cognitive and emotional states. These data inform BIM-driven alterations that facilitate adaptable, customized, and sustainability-oriented architectural solutions. The results highlight the ability of BCI–BIM integration to create dynamic, occupant-responsive environments that enhance well-being, promote energy efficiency, and minimize environmental impact. The primary contribution of this work is the demonstration of the viability of neuro-responsive architecture, wherein cognitive input from Brain–Computer Interfaces enables real-time modifications to architectural designs. This technique enhances built environments’ flexibility and user-centered quality by integrating occupant preferences and mental states into the design process. Furthermore, integrating BCI and BIM technologies has significant implications for advancing sustainability and facilitating the design of energy-efficient and ecologically responsible residential areas. The study offers practical insights for architects, engineers, and construction professionals, providing a method for implementing BCI–BIM systems to enhance user experience and promote sustainable design practices. The research examines ethical issues concerning privacy, data security, and informed permission, ensuring these technologies adhere to moral and legal requirements. The study underscores the transformational potential of BCI–BIM integration while acknowledging challenges related to data interoperability, integrity, and scalability. As a result, ongoing innovation and rigorous ethical supervision are crucial for effectively implementing these technologies. The findings provide practical insights for architects, engineers, and industry professionals, offering a roadmap for developing intelligent and ethically sound design practices.