Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturingfirm are vital for the rapid and accurate diagnosis of work performance,particularly during the training of a new worker.Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques.Despite widespread com-puter vision-based approaches,it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where cam-era deployment is problematic.Through the use of wearable inertial sensors,we propose a deep learning method for automatically recognizing the activities of construction workers.The suggested method incorporates a convolutional neural network,residual connection blocks,and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as con-struction worker tasks.The proposed approach has been evaluated using standard performance measures,such as precision,F1-score,and AUC,using a publicly available benchmark dataset known as VTT-ConIoT,which contains genuine con-struction work activities.In addition,standard deep learning models(CNNs,RNNs,and hybrid models)were developed in different empirical circumstances to compare them to the proposed model.With an average accuracy of 99.71%and an average F1-score of 99.71%,the experimentalfindings revealed that the suggested model could accurately recognize the actions of construction workers.Furthermore,we examined the impact of window size and sensor position on the identification efficiency of the proposed method.

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

Motion-based analysis for construction workers using biomechanical methods

Sustaining awkward postures and overexertion are common factors in construction industry that result in work-related injuries of workers. To address there safety and health issues, conventional observational methods on the external causes are tedious and subjective, while the direct measurement on the internal causes is intrusive leading to productivity reduction. Therefore, it is essential to construct an effective approach that maps the external and internal causes to realize the non-intrusive identification of safety and health risks. This research proposes a theoretical method to analyze the postures tracked by videos with biomechanical models. Through the biomechanical skeleton representation of human body, the workload and joint torques are rapidly and accurately evaluated based on the rotation angles of joints. The method is then demonstrated by two case studies about(1) plastering and(2) carrying. The experiment results illustrate the changing intramuscular torques across the construction activities in essence, validating the proposed approach to be effective in theory.

Examining the Impact of Welfare Facilities on Building Construction Employee’s Performance in the Eastern Region of Ghana

Building Construction employees essentially required sufficient welfare facilities such as a place for washing, shower, change over, eat and drink, a place to store and hang clothing. Yet, these fundamental necessities are regularly disregarded. Decent welfare facilities can absolutely promote health and well-being and can aid to avert contracting diseases at building construction site. The aim of this study is to examine the impact of welfare facilities on building construction workers performance in the Eastern Region Ghana. The objectives were to identify welfare facilities provided by building construction firms, evaluate the satisfaction level of building construction firms’ employees with welfare facilities in Eastern Region, and determine the relationship between provision of welfare facilities and employee’s performance. The study employed convenient sample to investigate 80 building construction employees through questionnaires administration. Data generated from the survey was further analyzed using SPSS, weighted mean formula was used to determine the mean ranking, and descriptive and inferential statistics such as mean score, percentages, frequencies and chi square were used. The study reveals that the extent of welfare facilities at various construction sites was almost unavailability of sanitary and toilet facilities, unavailability of washing facilities, and unavailability of changing room, whiles drinking water and locker facilities were available but not sufficient. The study further finds that construction employees are dissatisfied with the condition of welfare facilities provided at their various work places. The study finally concludes that if workers are provided with decent welfare facilities at various building construction sites, it will motivate them to improve performance. The study recommends that metropolitan, municipal and district authority (MMDAs) should set a task force to check the implementation of welfare facilities at construction site as the results are pointing woeful insufficiency of welfare facilities at Eastern Region construction firms in Ghana.

BRIDGING THE DISTANCE

Construction workers finish the final section of the Taitema Lake Bridge, part of the Golmud-Korla railway in northwest China＇s Xinjiang Uygur Autonomous Region, on August 17 after two years of construction. The railway, connecting Golmud in Qinghai Province and Korla in Xinjianp. will cut the travel time between the two cities from 26 to 12 hours.