A short review of the SDKs and wearable devices to be used for AR application for industrial working environment

During the last two decades, and especially the last few years, augmented reality helps workers manage safety risks on site and prevent injuries and increase the efficiency of safety. This review on the presently existing SDKs and their features is aimed at finding the most efficient AR SDK that would be suitable and would correspond to the purpose of the AR application for industrial working environment in terms of safety. The summarized information of the world\u2019s most widely used platforms for AR development, with support for leading phones, tablets and eyewear, SDKs presently available on the market is intended to help developers to create their AR application as well as to which parameters one should pay attention to, when building augmented reality applications for industrial use

Preliminary human safety assessment (PHSA) for the improvement of the behavioral aspects of safety climate in the construction industry

Occupational safety in the construction industry still represents a relevant problem at a global level. In fact, the complexity of working activities in this sector requires a comprehensive approach that goes beyond normative compliance to guarantee safer working conditions. In particular, empirical research on the factors influencing the unsafe behavior of workers needs to be augmented. Thus, the relationship between human factors and safety management issues following a bottom-up approach was investigated. In particular, an easy-to-use procedure that can be used to better address workers' safety needs augmenting the company's safety climate and supporting safety management issues was developed. Such an approach, based on the assessment of human reliability factors, was verified in a real case study concerning the users of concrete mixer trucks. The results showed that the majority of human failures were action and retrieval errors, underlining the importance of theoretical and practical training programs as a means to improve safety behavior. In such a context, information and communication activities also resulted beneficially to augment the company's safety climate. The proposed approach, despite its qualitative nature, allows a clearer understanding of workers' perceptions of hazards and their risk-taking behavior, providing practical cues to monitor and improve the behavioral aspects of safety climate. Hence, these first results can contribute to augmenting safety knowledge in the construction industry, providing a basis for further investigations on the causalities related to human performances, which are considered a key element in the prevention of accidents

Sensor-Based Safety Performance Assessment of Individual Construction Workers

Over the last decade, researchers have explored various technologies and methodologies to enhance worker safety at construction sites. The use of advanced sensing technologies mainly has focused on detecting and warning about safety issues by directly relying on the detection capabilities of these technologies. Until now, very little research has explored methods to quantitatively assess individual workers’ safety performance. For this, this study uses a tracking system to collect and use individuals’ location data in the proposed safety framework. A computational and analytical procedure/model was developed to quantify the safety performance of individual workers beyond detection and warning. The framework defines parameters for zone-based safety risks and establishes a zone-based safety risk model to quantify potential risks to workers. To demonstrate the model of safety analysis, the study conducted field tests at different construction sites, using various interaction scenarios. Probabilistic evaluation showed a slight underestimation and overestimation in certain cases; however, the model represented the overall safety performance of a subject quite well. Test results showed clear evidence of the model’s ability to capture safety conditions of workers in pre-identified hazard zones. The developed approach presents a way to provide visualized and quantified information as a form of safety index, which has not been available in the industry. In addition, such an automated method may present a suitable safety monitoring method that can eliminate human deployment that is expensive, error-prone, and time-consuming

Deploying AI Applications to Multiple Environments: Coping with Environmental, Data, and Predictive Variety

Deploying Artificial Intelligence (AI) proves to be challenging and resource-intensive in practice. To increase the economic value of AI deployments, organizations seek to deploy and reuse AI applications in multiple environments (e.g., different firm branches). This process involves generalizing an existing AI application to a new environment, which is typically not seamlessly possible. Despite its practical relevance, research lacks a thorough understanding of how organizations approach the deployment of AI applications to multiple environments. Therefore, we conduct an explorative multiple-case study with four computer vision projects as part of an ongoing research effort. Our preliminary findings suggest that new environments introduce variety, which is mirrored in the data produced in these environments and the required predictive capabilities. Organizations are found to cope with variety during AI deployment by 1) controlling variety in the environment, 2) capturing variety via data collection, and 3) adapting to variety by adjusting AI models

Synergies Between Lean Construction and Artificial Intelligence: AI Driven Continuous Improvement Process

Both, Lean Construction (LC) techniques and Artificial Intelligence (AI) methods strive for the continuous improvement of production systems in projects and organizations. A combined implementation of both approaches is an ongoing research area. Therefore, the question arises as to whether the added value generated by implementing both approaches jointly is greater than the added value generated by implementing them independently and what is the significance of people in their combined use. This paper explores theoretically the potential of synergies between LC and AI in the AEC sector with exemplary use cases as well as their resulting effects. Humans play a crucial role as interface between a combined use of both of them. As a result, a framework containing LC, AI and people is formed as basis for further combined developments. Therefore, change management, an area in which Lean has spent several years developing, can help both approaches gain traction. With the results, targeted applications can be developed, and practice can be supported

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances

Autonomous Reading of Gauges in Unstructured Environments

This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations

Hazard Recognition and Construction Safety Training Efficacy Study Using Virtual Reality (VR)

The majority of construction site incidents occur due to a lack of hazard awareness among workers on jobsites. This lack of awareness is despite mandatory construction safety training, typically in the form of written content (safety manuals) or of images depicting hazards. To reduce jobsite injuries and fatalities, general contractors have started adopting Virtual Reality (VR) to impart safety training to jobsite personnel. VR safety training is typically an immersive simulation comprising potential safety hazards embedded into a virtual jobsite; users are required to identify these hazards within a specified time frame with the expectation that they will be more adept at recognizing hazards on an actual jobsite, resulting in a fewer number of accidents. This study seeks to identify the actual impacts of VR on construction safety awareness among participants. The research addresses the following question: Does VR safety training increase hazard recognition awareness to a greater extent than conventional safety training? The method used for this research included: (a) assessing participants’ construction safety awareness after receiving VR training and comparing it against their past construction safety awareness; (b) assessing participants’ construction safety awareness after receiving conventional training and comparing it against their past construction safety awareness, and (c) comparing the delta or level of improvement observed in part (a) against levels of improvement observed in part (b). The research objective was to determine if VR training can offer greater improvement in safety awareness. Participants were asked to complete a multiple-choice Qualtrics questionnaire. The results of the study showed a statistically significant knowledge gain advantage with the use of VR

Visión computacional en la industria de la construcción: identificación de equipos de seguridad en obras mediante el uso de deep learning

La industria de la construcción es uno de los sectores que expone la vida de los operarios en constante peligro debido a las condiciones laborales que esta demanda como el trabajo en alturas, manejo de maquinaria pesada, entre otros. El uso de equipos de protección colectiva y personal es una medida de seguridad para resguardar la vida de los operarios frente a caídas, colisiones, entre otros accidentes. No obstante, en campo existen actitudes inapropiadas por parte del personal de obra pues estos tienden a retirarse los equipos de seguridad, debido a la disconformidad que produce su peso, el cambio de temperatura, entre otros factores. En efecto, actualmente el control de estos comportamientos es exhaustivo, pues involucra monitorear múltiples actividades proactivamente a lo largo de la jornada laboral. Este estudio propone evaluar la efectividad de la tecnología deep learning en automatizar el reconocimiento de estos equipos de seguridad para comunicar a los supervisores de campos sobre el uso inapropiado de estos objetos y, de esta manera, controlar los accidentes de obra. En consecuencia, se desarrolló una base de datos que comprende imágenes de equipos de seguridad en obra bajo diferentes condiciones visuales: variedad de intraclase (posturas, color, contexturas, estaturas, etc.), intensidades de iluminación, oclusiones, aglomeraciones, entre otros efectos. Este entregable se justifica debido a que en comparación con la literatura se analizó una mayor variedad de equipos de seguridad y se empleó para entrenar y evaluar tres algoritmos más recurridos en la bibliografía (VGG-16, Resnet-18 y Inception-V3), debido al desempeño de sus resultados. Específicamente, el performance del prototipo Inception-V3 alcanzó un valor de 84% en accuracy empleando el set de datos de escala regular. Este desempeño indica que las metodologías en aprendizaje profundo pueden contribuir a monitorear equipos de seguridad de obra al disponer de mayor datos, seleccionando modelos más sofisticados y siguiendo las recomendaciones en este documento para evitar confusiones en la clasificación de objetos. Asimismo, existen dos contribuciones adicionales. En primer lugar, se realizó un resumen del estado del arte sobre las aplicaciones actuales de la visión computacional en el sector construcción con el objetivo de orientar a otros proyectos a seleccionar un tema de estudio, identificar los logros alcanzados, responder a las limitaciones encontradas y reconocer buenas prácticas. En segundo lugar, el set de base de datos desarrollado presenta una mayor variedad de tipos de EPP’s y EPC’s, respecto a la literatura, y está disponible a solicitud con el objetivo de estandarizar la existencia de un set de datos propio para el sector construcción y facilitar la aplicación de la visión computacional en esta industria