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The physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica.

The viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance.

Results show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost.

Suggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment.

This study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.

Work-related musculoskeletal disorders constitute a severe problem in the construction industry. Workers' lower backs are often affected by heavy or repetitive lifting and prolonged awkward postures. Exoskeletal interventions are effective for tasks involving manual lifting and repetitive movements. This study aims to examine the potential of a postural-assist exoskeleton (a passive exoskeleton) for manual material handling tasks.

From an experimental observation of participants, the effects of postural-assist exoskeleton on tasks and workers were measured. Associated benefits of the exoskeleton were assessed through task performance, range of motion and discomfort.

Findings suggest that the exoskeleton influenced discomfort significantly, however range of motion decreased with lifting tasks. The reduced back flexion and increased hip flexion were also indicatives of the participants' responsiveness to the feedback from the exoskeleton. In addition, task completion time increased by 20%, and participants' back pain did not reduce.

The work tasks were performed in a controlled laboratory environment and only wearable inertia measurement units (IMUs) were used to assess the risk exposures of the body parts.

This study opens a practical pathway to human-exoskeleton integration, artificial regeneration or enablement of impaired workforce and a window toward a new order of productivity scaling. Results from this study provide preliminary insights to designers and innovators on the influence of postural assist exoskeleton on construction work. Project stakeholders can be informed of the suitability of the postural assist exoskeletons for manual material handling tasks.

Little has been reported on the benefits and impact of exoskeletons on tasks' physical demands and construction workers' performance. This study adds value to the existing literature, in particular by providing insights into the effectiveness and consequences of the postural-assist exoskeleton for manual material handling tasks.

This study investigates building information modelling (BIM) penetration in quantity surveying (QS) practice by examining the significance attached to the benefits and barriers of BIM adoption, BIM capabilities and future directions of BIM in QS processes. A popular opinion amongst construction researchers is that BIM has the capacity to revolutionize the industry. The study draws-out information in the literature regarding discipline-specific penetration of BIM.

Snowball sampling method was used to obtain information through a survey questionnaire. A total of 73 participants, largely quantity surveyors in Western Australia, took part in the study. Reductionist methodology was used to identify key variables of QS-BIM competencies that are most significant statistically.

BIM does not impose additional difficulties to traditional QS processes. Adherence to standard method of measurement and limited market demand do not hinder BIM deployment significantly. Quantity surveyors are able to use BIM to support their professional services once definitive design models are involved. In addition, the study identifies BIM penetration barriers to include constraints caused by centralised database management and interoperability issues, limitations imposed by market drivers, lack of in-house expertise to manage modelling needs and limited capability in software management.

Future opportunities for skill development are in the areas noted in the findings. Whilst many studies have reported resistance and widespread scepticism amongst some construction disciplines regarding BIM adoption, this study finds BIM penetration in QS practice is considerable, a direction that could trigger further novel innovations.

The methodology reported in the study is novel. In addition, findings from the study inspires other discipline-specific studies to articulate their BIM-penetration trends so that t broad areas of construction can develop a balanced strategy around BIM and innovation development.

Recognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.

A lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.

Results indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.

The study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.

This study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.

Concrete workers perform physically demanding work in awkward postures, exposing their backs to musculoskeletal disorders. Back-support exoskeletons are promising ergonomic interventions designed to reduce the risks of back disorders. However, the suitability of exoskeletons for enhancing performance of concrete workers has not been largely explored. This study aims to assess a passive back-support exoskeleton for concrete work in terms of the impact on the body, usability and benefits of the exoskeleton, and potential design modifications.

Concrete workers performed work with a passive back-support exoskeleton. Subjective and qualitative measures were employed to capture their perception of the exoskeleton, at the middle and end of the work, in terms of discomfort to their body parts, ease of use, comfort, performance and safety of the exoskeleton, and their experience using the exoskeleton. These were analyzed using descriptive statistics and thematic analysis.

The exoskeleton reduced stress on the lower back but caused discomfort to other body parts. Significant correlations were observed between perceived discomfort and usability measures. Design modifications are needed to improve the compatibility of the exoskeleton with the existing safety gears, reduce discomfort at chest and thigh, and improve ease of use of the exoskeleton.

The study was conducted with eight concrete workers who used the exoskeleton for four hours.

This study contributes to existing knowledge on human-wearable robot interaction and provides suggestions for adapting exoskeleton designs for construction work.

Carpenters are constantly vulnerable to musculoskeletal disorders. Their work consists of subtasks that promote nonfatal injuries and pains that affect different body segments. The purpose of this study is to examine ergonomic exposures of carpentry subtasks involved in floor framing, how they lead to musculoskeletal injuries, and how preventive and protective interventions around them can be effective.

Using wearable sensors, this study characterizes ergonomic exposures of carpenters by measuring and analyzing body movement data relating to major subtasks in carpentry flooring work. The exposures are assessed using Postural Ergonomic Risk Assessment classification, which is based on tasks involving repetitive subtasks and nonstatic postures.

The findings of this paper suggest severe risk impositions on the trunk, shoulder and elbow as a result of the measuring and marking and cutting out vent locations, as well as in placing and nailing boards into place.

Because of the type and size of wearable sensor used, only results of risk exposures of four body-parts are presented.

This study draws insights on how to benchmark trade-specific measurement of work-related musculoskeletal disorders. Safety efforts can be targeted toward these risk areas and subtasks. Specifically, results from these will assist designers and innovators in designing effective and adaptable protective interventions and safety trainings.

Extant studies have failed to provide adequate evidence regarding the relationships between subtasks and musculoskeletal disorders; they have only mimicked construction tasks through laboratory experimental scenarios. This study adds value to the existing literature, in particular by providing insights into hazards associated with floor carpentry subtasks.

Low back disorders are more predominant among construction trade workers than their counterparts in other industry sectors. Floor layers are among the top artisans that are severely affected by low back disorders. Exoskeletons are increasingly being perceived as ergonomic solutions. This study aims to compare the efficacy of passive and active back-support exoskeletons by measuring range of motion, perceived discomfort, usability, perceived rate of exertion and cognitive load during a simulated flooring task experiment.

In this study eight participants were engaged in a repetitive timber flooring task performed with passive and active back-support exoskeletons. Subjective and objective data were collected to assess the risks associated with using both exoskeletons. Descriptive statistics were used for analysis. Scheirer-Ray-Hare test and Wilcoxon signed-rank test were adopted to compare the exoskeleton conditions.

The results show no significant differences in the range of motion (except for a lifting cycle), perceived level of discomfort and perceived level of exertion between the two exoskeletons. Significant difference in overall cognitive load was observed. The usability results show that the active back-support exoskeleton made task execution easier with less restriction on movement.

The flooring task is simulated in a laboratory environment with only eight male participants.

This study contributes to the scarce body of knowledge on the usage comparison of passive and active exoskeletons for construction work.

Low back disorder is one of the most prevalent and costly injuries in the construction industry. Back-support exoskeletons are increasingly perceived as promising solutions. However, the intended benefits of exoskeletons may not be realized if intention-to-use the device is low. Social influence could increase intention-to-use exoskeletons. This study aims to evaluate the impact of social influence on construction workers' intention-to-use back-support exoskeletons.

A field study involving 37 construction workers was conducted, with workers who used exoskeleton for one week, and their peers and supervisors. Data were collected using questionnaires and semi-structured interviews, and analyzed using descriptive statistics and thematic analysis, respectively.

The workers felt that the exoskeleton is easy to use and the functions are well integrated. Workers' intention-to-use exoskeleton was mainly influenced by employers providing and requiring the use of the device. The attitude of the workers and the perception of peers and supervisors did not have a significant impact on workers' intention-to-use exoskeleton, whereas the subjective norm of construction workers had a positive impact on the intention-to-use exoskeletons.

The study involved only 37 workers, including 15 workers who used the exoskeleton, and 14 peers and 8 supervisors of the workers.

This study contributes to existing knowledge on the influence of social influence on intention-to-use exoskeletons. The study also highlights how exoskeleton designs and the construction workplace can influence behavioral intention-to-use exoskeletons.

Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

Construction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.

This paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).

Results show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.

Only acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.

Little has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.