Rework remains an ever-present reality in construction projects. How rework data is defined, its format, location, and quantification contribute to the difficulty in managing its risks. This paper examines the challenges and acquired learnings encountered while attempting to create and curate a domain rework ontology in construction. An explanatory case study approach utilizing the lens of pragmatism is used to develop a rework ontology in a real-life transport infrastructure mega-project procured using a program alliancing delivery strategy. As part of an alliance's continuous improvement strategy, it aims to redress its rework problem by assessing its risk pathways and managing them using a semantic model through an ontology. We use a hybrid approach to develop an ontology. The Correlation Explanation (CorEx) topic modelling approach, a machine learning method, is used to help define our ontology's scope and generate a rework taxonomy representative of practice. The theoretical and practical implications of developing a domain rework are also discussed. Our paper's main contribution is the propagation of a knowledge repository that others can learn from when developing an ontology and addressing the problem of rework, which is often embedded in the practice of construction.

The Quality I paradigm utilizes an error prevention strategy to avert rework in construction. The effectiveness of this paradigm is questionable as rework has become an innate feature of practice. If rework is to be mitigated in construction projects, a new paradigm is needed to challenge conventional thinking and offer a different perspective on managing errors. We introduce a new paradigm, Quality II, by drawing on a narrative review, emerging best practices deployed in construction, and contemporary developments in safety (e.g., Safety II and III). The implications of a Quality II paradigm for theory development and practice are also examined. The contributions of this article are twofold as we: (1) provide construction organizations with an approach for managing and learning how to handle (i.e., learning through) errors, and thus provide them with the ability to adapt and respond to varying conditions effectively; and (2) align Quality II with contemporary safety paradigms to offset competing demands enabling construction organizations to maximize the use of their limited resources better. By curbing rework, the performance and productivity of projects and the profitability of construction organizations will improve.

Machine learning (ML) and deep learning (DL) are both branches of AI. As a form of AI, ML automatically adapts to changing datasets with minimal human interference. Deep learning is a subset of ML that uses artificial neural networks to imitate the learning process of the human brain. The ‘black box’ nature of ML and DL makes their inner workings difficult to understand and interpret. Deploying explainable artificial intelligence (XAI) can help explain why and how the output of ML and DL models are generated. As a result, understanding a model’s functioning, behavior, and outputs can be garnered, reducing bias and error and improving confidence in decision-making. Despite providing an improved understanding of model outputs, XAI has received limited attention in construction. This paper presents a narrative review of XAI and a taxonomy of precepts and models to raise awareness about its potential opportunities for use in construction. It is envisaged that the opportunities suggested can stimulate new lines of inquiry to help alleviate the prevailing skepticism and hesitancy toward AI adoption and integration in construction.

Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as an approach to better understanding how AI solutions' outputs can improve decision-making. Evaluation frameworks to enable organizations to understand XAI's what, why, how, and when are yet to be developed. Thus, we aim to fill this void by developing a conceptual content , context , process, and outcome (CCPO) evaluation framework to justify XAI's adoption and effective management using construction organizations as a backdrop for the paper's setting. After introducing and describing the proposed novel CCPO framework for operationalizing XAI, we discuss its implications for future research. The contributions of our paper are twofold: (1) it highlights the need for organizations to embrace and enact XAI so that decision-makers and stakeholders can better understand why and how a specific prediction materializes; and (2) it provides a frame of reference for organizations to realize the business value and benefits of XAI.

The Quality I paradigm utilizes an error prevention strategy to avert rework in construction. The effectiveness of this paradigm is questionable as rework has become an innate feature of practice. If rework is to be mitigated in construction projects, a new paradigm is needed to challenge conventional thinking and offer a different perspective on managing errors. We introduce a new paradigm, Quality II, by drawing on a narrative review, emerging best practices deployed in construction, and contemporary developments in safety (e.g., Safety II and III). The implications of a Quality II paradigm for theory development and practice are also examined. The contributions of this paper are twofold as we: (1) provide construction organizations with a new approach for managing and learning how to handle (i.e., learning through) errors, and thus provide them with the ability to adapt and respond to varying conditions effectively; and (2) align Quality II with contemporary safety paradigms to offset competing demands enabling construction organizations to maximize the use of their limited resources better. By curbing rework, the performance and productivity of projects and the profitability of construction organizations will improve.

Within construction, we have become increasingly accustomed to relying on the benefits of digital technologies, such as Building Information Modelling, to improve the performance and productivity of projects. We have, however, overlooked the problems that technology is unable to redress. One such problem is rework, which has become so embedded in practice that technology adoption alone can not resolve the issue without fundamental changes in how information is managed for decision-making. Hence, the motivation of this paper is to bring to the fore the challenges of classifying and creating an ontology for rework that can be used to understand its patterns of occurrence and risks and provide a much-needed structure for decision-making in transport mega-projects. Using an exploratory case study approach, we examine 'how' rework information is currently being managed by an alliance that contributes significantly to delivering a multi-billion dollar mega-transport project. We reveal the challenges around location, format, structure, granularity and redundancy hindering the alliance's ability to classify and manage rework data. We use the generative machine learning technique of Correlation Explanation to illustrate how we can make headway toward classifying and then creating an ontology for rework. We propose a theoretical framework utilising a smart data approach to generate an ontology that can effectively use business analytics (i.e., descriptive, predictive and prescriptive) to manage rework risks.

There has been a wealth of research that has examined the nature of rework in construction. Progress toward addressing the rework problem has been limited—it still plagues practice, adversely impacting a project's performance. Almost all rework studies have focused on determining its proximal or root causes and therefore have overlooked the conditions that result from its manifestation. In filling this void, this paper draws upon our previous empirical studies, amongst others, to provide a much-needed theoretical framing to understand better why rework occurs, what its consequences are, and how it can be mitigated during construction. The theoretical framing we derive from our review provides construction organizations and their projects with a realization that the journey to mitigating rework begins with creating an error-mastery culture comprising authentic leadership, psychological safety, an error-management orientation, and resilience. We suggest that, once an error-mastery culture is established within construction organizations and their projects, they will be better positioned to realize the benefits of the techniques, tools, and technologies espoused to address rework, such as the Last Planner® and building information modeling. We also provide directions for future research and identify implications for practice so that strides toward rework mitigation in construction can be made.

Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no evaluation frameworks have been propagated to enable construction organisations to understand the what, why, how, and when of XAI. Our paper aims to fill this void by developing a content, context, process, and outcome evaluation framework that can be used to justify the adoption and effective management of XAI. After introducing and describing this novel framework, we discuss its implications for future research. While our novel framework is conceptual, it provides a frame of reference for construction organisations to make headway toward realising XAI business value and benefits.

Online learning platforms are integrated systems designed to provide students and teachers with information, tools and resources to facilitate and enhance the delivery and management of learning. In recent years platform designers have introduced gamification and multimodal interaction as ways to make online courses more engaging and immersive. Current Web-based platforms provide a limited degree of immersion in learning experiences, thereby diminishing potential learning impact. To improve immersion, it is necessary to stimulate some or all the human senses by engaging users in an environment that perceptually surrounds them and allows intuitive and rich interaction with other users and its content. Learning in these collaborative virtual environments (CVEs) can be aided by increasing motivation and engagement through the gamification of the educational task. This rich interaction that combines multimodal stimulation and gamification of the learning experience has the potential to draw students into the learning experience and improve learning outcomes. This paper presents the results of an experimental study designed to evaluate the impact of multimodal real-time interaction on user experience and learning of gamified educational tasks completed in a CVE. Secondary school teachers and students participated in the study. The multimodal CVE is an accurate reconstruction of the European Parliament in Brussels, developed using the REVERIE (Real and Virtual Engagement In Realistic Immersive Environment) framework. In the study, we compared the impact of the VR parliament to a non-multimodal control (an educational platform called Edu-Simulation) for the same educational tasks. Our results show that the multimodal CVE improves student learning performance and aspects of subjective experience when compared to the non-multimodal control. More specifically it resulted in a more positive effect on the ability of the students to generate ideas compared to a non-multimodal control. It also facilitated a sense of presence (strong emotional and a degree of spatial) for students in the VE. The paper concludes with a discussion of future work that focusses on combining the best features of both systems in a hybrid system to increase its educational impact and evaluate the prototype in real-world educational scenarios.

This paper looks at the challenge of performing Computational Fluid Dynamic simulations against models of real world environments and objects. The difficulty for these simulations is the effort required to render complex objects and environments, as well as the computational power required to solve them. To assist with the first problem, this paper presents a process for importing Building Information Models or 3D CAD models into ANSYS workbench, a popular CFD environment in industry. In order to address computational concerns, this paper also introduces and discusses a system, LODOS, designed to aid in importation and set-up of high complexity models by selectively reducing geometric complexity. This paper presents preliminary results from the use of this system to import two Building Information Models into ANSYS Workbench, showing the current strengths of the system, as well as discussing its current limitations.

Information-centric networking (ICN) has long been advocating for radical changes to the IP-based Internet. However, the upgrade challenges that this entails have hindered ICN adoption. To break this loop, the POINT project proposed a hybrid, IP-over-ICN, architecture: IP networks are preserved at the edge, connected to each other over an ICN core. This exploits the key benefits of ICN, enabling individual network operators to improve the performance of their IP-based services, without changing the rest of the Internet. We provide an overview of POINT and outline how it improves upon IP in terms of performance and resilience. Our focus is on the successful trial of the POINT prototype in a production network, where real users operated actual IP-based applications.

The efficient provision of IPTV services requires support for IP multicasting and IGMP snooping, limiting such services to single operator networks. Information-Centric Networking (ICN), with its native support for multicast seems ideal for such services, but it requires operators and users to overhaul their networks and applications. The POINT project has proposed a hybrid, IP-over-ICN, architecture, preserving IP devices and applications at the edge, but interconnecting them via an SDN-based ICN core. This allows individual operators to exploit the benefits of ICN, without expecting the rest of the Internet to change. In this paper, we first outline the POINT approach and show how it can handle multicast-based IPTV services in a more efficient and resilient manner than IP. We then describe a successful trial of the POINT prototype in a production network, where real users tested actual IPTV services over both IP and POINT under regular and exceptional conditions. Results from the trial show that the POINT prototype matched or improved upon the services offered via plain IP.

REVERIE (REal and Virtual Engagement in Realistic Immersive Environments) is a research project with the aim to build a safe, collaborative, online environment which brings together realistic inter-personal communication and interaction. The REVERIE platform integrates cutting-edge technologies and tools, such as social networking services, spatial audio adaptation techniques, tools for creating personalized lookalike avatars, and artificial intelligence (A.I) detection features of the user’s affective state into two distinct use cases. The first shows how REVERIE can be used in educational environments with an emphasis on social networking and learning. The second aims to emulate the look and feel of real physical presence and interaction for entertainment and collaborative purposes. This paper presents an expert evaluation of the first use case by potential users of REVERIE (teachers and students). Finally, the potential of REVERIE for game-based learning is discussed followed by an overview of the actionable recommendations that emerged as a result of the expert review.

The design and construction industry is moving towards Building Information Models (BIM) that provide all of the strengths of traditional 3D CAD with an added layer of data allowing new and powerful applications. We investigate the concept of using the data within BIM to better explore security design and considerations. We achieve this by first graphing the physical entities of BIM to capture their relational representation as nodes and links. This graph representation will facilitate the use of graph theory or agent-based simulation to assist in the analysis of the static and dynamic behaviour of the environment around the BIM. We also demonstrate an application of graphing by investigating the use of BIM to explore automated infrastructure security design and consideration via red-teaming. The intent is to make security analysis easier and a process that can be carried out during the design phase of a project, even by non-expert users.

Computational Red Teaming (CRT) is a computational approach to test defences. In this paper, we use develop a CRT system to assess the physical security of any building, provided the building is designed with Building Information Models (BIM). BIMs are similar to 3D Computer Aided Design (CAD) models but with the addition of extra layers of information on the building system and components. Through a demonstration attack on a facility, this paper shows how a CRT approach to physical security assessment leads to a deeper and wider analysis of the building. The Red Team attacks every room under varying capabilities. The Blue Team analyses the attack data and strengthens the defences against further attack. Each attack-defend cycle yields a rich set of data that can be analysed by the user. We show that our algorithm performs at O(KLn(m+nlogn)) time. We present the effectiveness of our system based on recent developments in physical security assessment.

It has been argued by many that the Future Internet should address information at the core of its operation. Prototypes have emerged to embody this new paradigm. Applications for such networks, however, are noted primarily by their absence. In spite of an appetite for Information-Centric Networking (ICN) applications, relatively little has come to fruition. We suggest that this is due to an unfavorable development environment, requiring applications to interface with the ICN substrate directly. This paper aims to answer this shortcoming by providing a middleware layer that aids the development of more advanced applications. We also present an application that leverages the middleware and answers a real-world problem concerning personalised media delivery. We argue that the development of this, and potentially other, application(s) is aided by the presence of such an application environment.