Riesgo de desórdenes músculo esquelético en empresa metal-mecánica. Caso: costa caribe colombiana.

The present study was born as an initiative of the management of a company in the metal-mechanic sector of the ColombianCaribbean coast, with the intention of knowing and improving the labor realities of the working population. The purpose of this study is toevaluate the risk of musculoskeletal disorders DME, analyzing individual postures and referred symptoms of 17 jobs that develop 61 workers.The study has a quantitative, empirical and descriptive approach and was developed in four phases: the first consisted in the recognition of theworking conditions and tasks performed, the second in the application of the Nordic Musculoskeletal Disorder questionnaire, the third in theapplication of the REBA method to each job, and the fourth in the analysis of the information. As a result it was found that there is a high levelof risk of immediate intervention DME for the welding assistant and roofer. Finally, the study concludes on the need to implement anepidemiological surveillance system for the prevention of musculoskeletal disorders.El presente estudio nace como iniciativa de la gerencia de una empresa del sector metalmecánico de la costa caribe colombiana,con la intención de conocer y mejorar las realidades laborales de la población trabajadora. El propósito de este estudio es evaluar el riesgo dedesórdenes músculo esquelético DME, analizando las posturas individuales y los síntomas referidos de 17 puestos de trabajo que desarrollan61 trabajadores. El estudio tiene un enfoque cuantitativo, empírico y de tipo descriptivo y se desarrolló en cuatro fases: la primera consistió enel reconocimiento de las condiciones de trabajo y tareas que se realizan, la segunda en la aplicación del cuestionario nórdico para trastornosmúsculo esquelético, la tercera en la aplicación del método REBA a cada puesto de trabajo, y la cuarta, en el análisis de la información. Comoresultado se encontró que existe nivel de riesgo alto de DME de inmediata intervención para el auxiliar de soldadura y techero. Por último, elestudio concluye en la necesidad de implementar un sistema de vigilancia epidemiológica para la prevención de desórdenes músculo esquelético

An in-depth investigation of digital construction technologies from a building economics perspective

While initial costs in building economics cover a small portion of the costs incurred during its life-cycle, most occur in construction, operation, and subsequent processes. Despite its numerous contributions to building economics, the construction industry is slowly adapting to digital technologies. To overcome the barriers and crown the assets with their proper management, dynamic applications of digital tools and techniques of Industry 4.0 need to emerge in the construction industry. Therefore, this study aims to present an integrative approach that combines quantitative and qualitative analysis techniques to critically review the available literature on the potential contributions of digital construction technologies to building economics through the post-design phases of the life cycle. The primary focus of the investigation is how digital technologies can overcome prevalent problems and how they can impact building economics. The study contributes to the field by providing an awareness that will inform researchers and practitioners of the trends, gaps, and more profound exchange of ideas in future research efforts

Personalized method for self-management of trunk postural ergonomic hazards in construction rebar ironwork

Construction rebar workers face postural ergonomic hazards that can lead to work-related Lower Back Disorders (LBDs), primarily due to their prolonged awkward working postures required by the job. In a previous study, Wearable Inertial Measurement Units (WIMUs)-based Personal Protective Equipment (PPE) was developed to alert workers when their trunk inclination holding time exceeded acceptable thresholds as defined in ISO standard 11226:2000. However, subsequent field testing identified PPE was ineffective for some workers because the adopted ISO thresholds were not personalized and did not consider differences in individual’s response to postural ergonomic hazards. To address this problem, this paper introduces a worker-centric method to assist in the self-management of work-related ergonomic hazards, based on data-driven personalized healthcare intervention. Firstly, personalized information is gathered by providing each rebar ironworker a WIMU-based personalized mobile health (mHealth) system to capture their trunk inclination angle and holding time data. Then, the captured individual trunk inclination holding times are analyzed by a Gaussian-like probability density function, where abnormal holding time thresholds can be generated and updated in response to incoming trunk inclination records of an individual during work time. These abnormal holding time thresholds are then adapted to be used as personalized trunk inclination holding time recommendations for an individual worker to self-manage their working postures, based on their own trunk inclination records. The proposed worker-centric method to assist in the self-management of ergonomic postural hazards leading to LBDs was field tested on a construction site over a three-month duration. The results of the paired t-tests indicate that posture scores evaluated by the Ovako Working Posture Analysis System (OWAS) significantly decrease when the personalized recommendation is applied, while increase again when the personalized recommendation is removed. Based on data-driven personalized healthcare intervention, the results demonstrate the significant potential of the proposed worker-centric self-management method for rebar workers in preventing and controlling postural ergonomic hazards during construction rebar ironwork

Semantic and spatio-temporal understanding for computer vision driven worker safety inspection and risk analysis

Despite decades of efforts, we are still far from eliminating construction safety risks. Recently, computer vision techniques have been applied for construction safety management on real-world residential and commercial projects; they have shown the potential to fundamentally change safety management practices and safety performance measurement. The most significant breakthroughs of this field have been achieved in the areas of safety practice observations, incident and safety performance forecasting, and vision-based construction risk assessment. However, fundamental theoretical and technical challenges have yet to be addressed in order to achieve the full potential of construction site images and videos for construction safety. This dissertation explores methods for automated semantic and spatio-temporal visual understanding of workers and equipment and how to use them to improve automatic safety inspections and risk analysis: (1) a new method is developed to improve the breadth and depth of vision-based safety compliance checking by explicitly classifying worker-tool interactions. A detection model is trained on a newly constructed image dataset for construction sites, achieving 52.9% mean average precision for 10 object categories and 89.4% average precision for detecting workers. Using this detector and new dataset, the proposed human-object interaction recognition model achieved 79.78% precision and 77.64% recall for hard hat checking; 79.11% precision and 75.29% recall for safety vest checking. The new model also verifies hand protection for workers when tools are being used with 66.2% precision and 64.86% recall. The proposed model is superior to methods relying on hand-made rules to recognize interactions or that reason directly on the outputs of object detectors. (2) to support systems that proactively prevent these accidents, this thesis presents a path prediction model for workers and equipment. The model leverages the extracted video frames to predict upcoming worker and equipment motion trajectories on construction sites. Specifically, the model takes 2D tracks of workers and equipment from visual data -based on computer vision methods for detection and tracking- and uses a Long Short-Term Memory (LSTM) encoder-decoder followed by a Mixture Density Network (MDN) to predict their locations. A multi-head prediction module is introduced to predict locations at different future times. The method is validated on an existing dataset TrajNet and a new dataset of 105 high-definition videos recorded over 30 days from a real-world construction site. On the TrajNet dataset, the proposed model significantly outperforms Social LSTM. On the new dataset, the presented model outperforms conventional time-series models and achieves average localization errors of 7.30, 12.71, and 24.22 pixels for 10, 20, and 40 future steps, respectively. (3) A new construction worker safety analysis method is introduced that evaluates worker-level risk from site photos and videos. This method evaluates worker state, which is based on workers' body pose, their protective equipment use, their interactions with tools and materials, the construction activity being performed, and hazards in the workplace. To estimate worker state, a visual-based Object-Activity-Keypoint (OAK) recognition model is proposed that takes 36.6% less time and 40.1% less memory while keeping comparably performances compared to a system running individual models for each sub-task. Worker activity recognition is further improved with a spatio-temporal graph model using recognized per-frame worker activity, detected bounding boxes of tools and materials, and estimated worker poses. Finally, severity levels are predicted by a trained classifier on a dataset of images of construction workers accompanied with ground truth severity level annotations. In the test dataset, the severity level prediction model achieves 85.7% cross-validation accuracy in a bricklaying task and 86.6% cross-validation accuracy for a plastering task

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants