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Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues.

Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks.

The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information.

This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

A quick, clear, and consistent automatic graphic display system has been developed for easy editing of complex concept subnetworks using only a floppy diskette drive and a PET2001–8 microcomputer. Initially implemented using Farradane's relations, it has an underlying data structure that facilitates merging with a general concept network. The primary display format is an enhanced triangular matrix; concepts may be sorted according to a standard citation order, and the display may also be automatically ‘compressed’.

Purpose: Curatorial consumption studies have hitherto focused on the consumption of family heirlooms. By exploring curatorial consumption within the context of vintage outlets, the authors extend its usage to other consumption sites, allowing them to further develop the construct.

Design/Methodology/Approach: Participant observation was employed at vintage outlets alongside in-depth interviews with 15 vintage traders incorporating object elicitation.

Findings: The authors identify the potential for curatorial consumption to help further develop understanding of individuals’ relationships with their possessions. The authors present a re-contextualization of curatorial consumption, which expands the term beyond caring for family heirlooms, allowing them to incorporate additional contexts. The authors identify vintage traders’ roles as guardians for their merchandise and their sense of responsibility to ensure objects’ circulation to future generations. The authors develop the findings around themes related to curation: acquisition, preservation, and transference. Running through these themes is an overarching concern for historical objects.

Originality/Value: While few studies loosely refer to curatorial consumption, the construct remains underdeveloped. The re-contextualization allows to unpack its potential to enhance understanding of individuals’ relationships with their possessions. In contrast to existing curatorial consumption work that emphasizes the sense of continuity with ancestors, the authors extend this to consider how connections with the past can be maintained beyond local family settings.

An application development framework for a software project based on fusion as an object‐oriented application development method is presented. An object‐oriented approach has been adopted for the design and implementation of the prototype interactive visual modelling system for building a visual presentation of a refinery process and creation of linear programming model for optimizing production decision variables. The main reason for this selection is the consideration of object‐oriented programming (OOP) as an obvious vehicle for the development of complex visual interactive modelling systems. The main dimensions of the framework are as follows: OO approach, fusion method, computer‐aided software engineering (CASE) tool, application development tool, GUI development tool, and C++ as an implementation language.

The 4D CAD model has been accepted for better conceptualizing and comprehending the sequences and spatial constraints in a construction schedule. The purpose of this paper is to identify the deficiencies of the visualization of the 4D CAD model and to propose improvements.

The presentation abilities of the existing 4D CAD model are analyzed and compared with the other conventional methods, namely Gantt chart, network diagram, and the calendar. Four aspects of the visualization are addressed, namely the overview of a schedule, the duration of an activity, the relationship of an activity, and the project progress tracking. The proposed improvements employed different visual properties of 3D CAD objects such as color, line weight, and line type to represent the different activities' performing statuses. A prototype of the 4D CAD model with enhanced visualization was developed on a construction project case.

The model evaluation showed that this development could enhance the visualization of the 4D CAD model and provide a more informative construction schedule.

It is anticipated that the 4D CAD model with these enhancements can substitute for conventional presentation methods of construction schedules.

Discusses the long existing and confusing problems of establishing the relationship of who is, and who if not, a dependent worker. Reflects developments which have occurred in British law as it affects the employment field, plus an evaluation and analysis of some of the different types of employment relationships which have evolved by examining, where possible, the status of each of these relationships. Concludes that the typical worker nowadays finds himself in a vulnerable position both economically and psychologically owing to the insecurity which exists.

In this paper we present a model‐based approach for the development of physical hypermedia applications, i.e. those mobile (Web) applications in which physical and digital objects are related and explored using the hypermedia paradigm. We describe an extension of the Object‐Oriented Hypermedia Design Method (OOHDM) and present an improvement of the popular Model‐View‐Controller (MVC) metaphor to incorporate the concept of located object we illustrate the idea with a framework implementation using Jakarta Struts. We first review the state of the art of this kind of software systems, stressing the need of a systematic design and implementation approach we briefly present a light extension to the OOHDM design approach, incorporating physical objects and “walkable” links. We next present a Web application framework for deploying physical hypermedia software and show an example of use. We evaluate our approach and finally we discuss some further work we are pursuing.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

This study aims to illustrate the potential of coworking spaces as one way to achieve optimal workplace arrangements and corporate real estate (CRE) agility, especially for large organizations. The authors suggest understanding coworking spaces from the boundary organization theory and organizational growth model.

This study takes a threefold theoretical approach: conducting a literature review and identifying the gaps in coworking studies for large organizations, applying the organizational boundary theory in tandem with organizational growth models in the context of coworking spaces as a part of the workplace ecology and identifying future research agendas for coworking studies.

This study proposes a conceptual framework of how coworking spaces can be viewed and used as a boundary object throughout the organizational growth phases. Besides, four major future research areas are proposed: case studies and/or empirical evidence of coworking spaces as CRE buffer zones and boundary objects for organizations, coworking space design and different formats of boundary object-infused collaboration, coworking space design and management for its own agility and flexibility and how coworking affects employees’ performance, health and well-being and professional training/mentoring.

For large organizations, there is a clear pressure to rethink CRE to increase workplace agility, flexibility and resilience, much accelerated with the recent COVID-19 pandemic. Understanding the effective use of coworking spaces as a part of CRE portfolios will help enhance corporates’ state and ability to reassess, realign and replan their CRE portfolios.

Many existing studies about coworking spaces are based on observations and self-reported justification at an individual level. Whether and how coworking can benefit companies at an organizational level is largely unstudied and worth more attention. This study illustrates a new theoretical understanding of how coworking spaces can be a part of CRE portfolios and bring potential benefits of inter and intraorganizational collaboration throughout the phases of organizational growth.