LUIGI RIDOLFI

Pubblicazioni

LUIGI RIDOLFI

 

3 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
2 4 Contributo in Atti di Convegno (Proceeding)
1 1 Contributo su Rivista
Anno
Risorse
2020
Development of a Digital Twin Model for Real-Time Assessment of Collision Hazards
Proceedings of the Creative Construction e-Conference (2020)
Autore/i: Messi, Leonardo; Naticchia, Berardo; Carbonari, Alessandro; Ridolfi, Luigi; Di Giuda, Giuseppe Martino
Editore: Attila Varga, Róbert Hohol, Gergely Szakáts
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: 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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290354 Collegamento a IRIS

2019
A DECISION SUPPORT SYSTEM FOR MULTI-CRITERIA ASSESSMENT OF LARGE BUILDING STOCKS
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
Autore/i: CARBONARI, Alessandro; CORNELI, ALESSANDRA; Martino DI GIUDA, Giuseppe; RIDOLFI, LUIGI; VILLA, Valentina
Classificazione: 1 Contributo su Rivista
Abstract: Both public administrations and private owners of large building stocks need to work out plans for the management of their property, while having to deal with yearly budget limitations. Particularly for the former, this is a rather critical challenge, since public administrations are given the responsibility of sticking to very strict budget distributions over the years. As a consequence, when planning the actions to be taken on their building stocks in order to comply with their current use and the legislation in-force, they need to classify refurbishment priorities. The aim of this paper is to develop a first tool based on Bayesian Networks that offers an effective decision support service for owners even in case some information is incomplete. This tool can be used to evaluate the compliance of existing buildings with the latest standards. The decision support platform proposed includes a multi-criteria evaluation approach combining several performance indicators, each of which related to a specific regulatory area. This tool can be applied to existing buildings, where the building with the lowest score shows the highest priority of intervention. Also, the platform performs an assessment of expected costs for required refurbishment or renovation actions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/268417 Collegamento a IRIS

2018
BIM-Based Decision Support System for the Mangement of Large Building Stocks
35th International Symposium on Automation and Robotics in Construction
Autore/i: Carbonari, Alessandro; Corneli, Alessandra; Di Giuda, Giuseppe; Ridolfi, Luigi; Villa, Valentina
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: While on the one hand the BIM methodology is an essential reference for the construction of new buildings, on the other hand it is receiving particular attention and interest also from owners of large building stocks who want to take advantage of the benefits of Building Information Modelling so as to have a coordinated system for the sharing of information and data. This, especially in a process that concerns the management and maintenance of a large building stocks, involves the processing of uncertain information in BIM, particularly when dealing with existing buildings, due to the lack of and/or incomplete documentation, entailing a significant investment in terms of time and additional costs. Therefore, to represent the reliability of existing building data, we suggest introducing a tool based on Bayesian Network that offers a valid decision support under conditions of uncertainty and is used to evaluate the compliance with the latest standard. This paper presents a process to provide an integrated database defined by a minimum information level that can be used both to extrapolate and query specific information from a digital building model and populate the decision model in order to evaluate the performance parameters of existing buildings which is based on a Multicriteria decision making approach (AHP).
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/263453 Collegamento a IRIS




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