Abstract

Triax’s Spot-r system was developed to tackle the construction industry’s unique safety and productivity challenges, stemming from a demonstrated lack of total jobsite visibility and automatically collected, easily accessible field data. Spot-r by Triax combines a proprietary mesh network with sensor technology to record and transmit previously unattainable workforce and equipment location, activity and safety data within a defined area – all in real-time. The Spot-r network is installed around the site and operates in a 900 MHz network, with a subset using a cellular connection to forward Spot-r sensor data to Triax’s custom, cloud dashboard. In the hazardous construction environment, the wearable Spot-r Clip detects worker slips, trips, or falls on site and sends automatic alerts to designated personnel. Workers can also trigger an alert to supervisors via their wearable device. In a situation that requires an evacuation, superintendents can activate the Spot-r EvacTag, which augments current evacuation procedures with a visual, high-decibel alarm. Real-time worker headcounts and floor- and zone-based location via the Spot-r dashboard eliminates time spent tracking down people or information and helps account for workers during an evacuation. Operating off the same network and working with the Spot-r Clip, the Spot-r EquipTag adheres to nearly any piece of equipment to track real-time location, utilization, and operator identity, providing a comprehensive view of how resources are interacting on site. Aggregate utilization data by operator, subcontractor, zone, or time period helps coordinate resources and minimize downtime. By automatically collecting real-time data from workers and equipment - without technical limitations such as wireless internet, project scope, or clear line of sight to the sky - Spot-r provides unprecedented visibility into site operations and safety, helping contractors build safer and smarter. Spot-r is currently being used on more than 40 sites by some of the nation’s leading contractors, including Turner and Gilbane.

Abstract

A phenomenological interpretation of the slope factors and lanslide mechanism represents the first stage for the assessment of landslide hazard at the slope scale. This requires processing, analysing and integrating a large set of multidisciplinary and heterogeneous data, obtained through diverse activities, among which: geological and geomorphological studies, geotechnical investigations and monitoring, topographic and structural damage surveys. The integration of such a variety of multisource data, to build up a sound conceptual model of the slope, can be particularly challenging, especially in geohydromechanical contexts characterised by a great spatial variability of soil properties and complex hydraulic boundary conditions, such as in the case of slopes composed of turbiditic formations. This paper presents a new methodological approach for the study of landslide hazard at the slope scale, based on the combined use of an open-source GIS platform and an in-house developed dashboard for the interactive visualisation and analysis of geotechnical laboratory data. The details of the GIS project and the potentiality of the data-analysis dashboard are described, highlighting the interoperability between the two digital tools. The proposed methodology is applied to a pilot site, the Pianello area in Bovino, in the souhteastern Apennines, a widely investigated hillslope composed of tectonised clayey turbidite, hosting a complex basin of slow-moving landslides.

Abstract

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence Intelligent traffic planning, the promotion of public transport and the improved interconnectedness of all road users in a city-wide communication infrastructure comprise the mobility characteristics of a smart city. Living in an era of Smart Cities emerging, we can make use of all the data available nowadays to better serve our lives with the best services. All the data generated every day by the Vehicles can be used to get a better insights to improve the quality of the available service as the creation of new routes, increase supervision on problematic areas, among others. This document intends to detail the project carried out at Carris, a public transportation company which operates Lisbon's buses, trams, and funiculars. The master thesis project aims to create a Business Intelligence solution to support planning decisions though the use of a Dashboard by presenting information about all relevant data collected on a bus daily route by the driver in order to get a better understating of a public transportation service and how can the organization improve them to rebuild schedules, create or eliminate routes, do some inspection on tickets validations inside the vehicles, etc. This project is supported by a Microsoft tool called Power BI that enables all the extraction, transformation and loading of data processes, based on an Azure SQL Data Warehouse based on Cloud services. The Business Dashboards are divided in 4 main areas inside the organization and will support Onboard Sales, Ticket validations, Route Planning and Onboard issues reported by the driver.