Dynamic modelling of a 3-CPU parallel robot via screw theory

The article describes the dynamic modelling of I.Ca.Ro., a novel Cartesian parallel robot recently designed and prototyped by the robotics research group of the Polytechnic University of Marche. By means of screw theory and virtual work principle, a computationally efficient model has been built, with the final aim of realising advanced model based controllers. Then a dynamic analysis has been performed in order to point out possible model simplifications that could lead to a more efficient run time implementation

Dynamic modelling of a 3-CPU parallel robot via screw theory

Abstract. The article describes the dynamic modelling of I.Ca.Ro., a novel Cartesian parallel robot recently designed and prototyped by the robotics research group of the Polytechnic University of Marche. By means of screw theory and virtual work principle, a computationally efficient model has been built, with the final aim of realising advanced model based controllers. Then a dynamic analysis has been performed in order to point out possible model simplifications that could lead to a more efficient run time implementation

Soil-structure interaction effects on the seismic response of multi-span viaducts

The paper focuses on the effects of soil-structure interaction in the seismic response of multi-span viaducts on pile foundations. Analyses are performed by means of the substructure approach: the soil-foundation systems are studied in the frequency domain to obtain the foundation input motion and the dynamic impedance functions; inertial interaction analyses are carried out in the time domain accounting for the material nonlinear behaviour. Suitable lumped parameter models are introduced to simulate the frequency dependent behaviour of the soilfoundation system. A specific procedure for selecting and scaling real ground motions is proposed and used for the definition of the spatial seismic input. The seismic response of bridges on compliant base is compared with that obtained from fixed base analyses discussing the significance of soil-structure interaction effects

Training of YOLO Neural Network for the Detection of Fire Emergency Assets

Building assets surveys are cost and time demanding and the majority of current methods still rely on manual procedures. New technologies could be used to support this task. The exploitation of Artificial Intelligence (AI) for the automatic interpretation of data is spreading throughout various application fields. However, a challenge with AI is the very large number of training images required for robustly detect and classify each object class. This paper details the procedure and parameters used for the training of a custom YOLO neural network for the recognition of fire emergency assets. The minimum number of pictures for obtaining good recognition performances and the image augmentation process have been investigated. In the end, it was found that fire extinguishers and emergency signs are reasonably detected and their position inside the pictures accurately evaluated. The use case proposed in this paper for the use of custom YOLO is the retrieval of as-is information for existing buildings. The trained neural networks are part of a system that makes use of Augmented Reality devices for capturing pictures and for visualizing the results directly on site

Development of a Digital Twin Model for Real-Time Assessment of Collisione Hazards

The AEC industry is nowadays one of the most hazardous industries in the world. The construction sector employees about 7% of the world’s work force but is responsible for 30-40% of fatalities. As statistics demonstrate, interferences between workers-on-foot and moving vehicles have caused several injuries and fatalities over the years. Despite safety organizational measures, passive safety devices imposed by regulations and efforts from training procedures, scarce improvements have been recorded. Recent research studies propose technology driven approaches as the key solutions to integrate standard health and safety management practices. This is motivated by the evidence that the dynamics of complex systems can hardly be predicted; rather a proactive approach to health and safety is more effective. Current technologies installed on construction equipment can usually react according to a strict logic, such as sending proximity alerts when workers and equipment are too close. Nevertheless, these approaches barely do make informed decisions in real-time, e.g. including the level of reactiveness of the endangered worker. In similar circumstances a digital twin of the construction site, updated by real-time data from sensors and enriched by artificial intelligence, can pro-actively support activities, forecasting dangerous scenarios on the base of several factors. In this paper a laboratory mock-up has been assumed as the test case, supported by a game engine, which is able to replicates the job site for the execution of bored piles. In such a scenario populated by an avatar of a sensor-equipped worker and a virtual driller, a Bayesian network, implemented within the game engine and fed in runtime by sensor data, works out collision probability in real-time in order to send warnings and avoid fatal accidents