ALESSANDRA CORNELI

Pubblicazioni

ALESSANDRA CORNELI

 

13 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
8 4 Contributo in Atti di Convegno (Proceeding)
4 1 Contributo su Rivista
1 8 Tesi di dottorato
Anno
Risorse
2021
A decision support system for scenario analysis in energy refurbishment of residential buildings
ENERGIES
Autore/i: Giretti, Alberto; Corneli, Alessandra; Naticchia, Berardo
Classificazione: 1 Contributo su Rivista
Abstract: The energy efficiency of buildings is a key condition in the implementation of national sustainability policies. Energy efficiency of the built heritage is usually achieved through energy contracts or renovation projects that are based on decisions often taken with limited knowledge and in short time frames. However, the collection of comprehensive and reliable technical information to support the decision process is a long and expensive activity. Approximate assessment methods based on stationary thermal models are usually adopted, often introducing unacceptable uncertainties for economically onerous contracts. Hence, it is important to develop tools that, by capitalizing on the operators’ experience, can provide support for fast and reliable assessments. The paper documents the development of a decision support system prototype for the management of energy refurbishment investments in the residential building sector that assists operators in the energy performance assessment, using a limited set of technical information. The system uses a Case Based paradigm enriched with probabilistic modelling to implement decision support within the corporate’s knowledge management framework.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/292653 Collegamento a IRIS

2020
Artificial Intelligence assisted Building Digitization using Mixed Reality
Autore/i: CORNELI, ALESSANDRA
Editore: Università Politecnica delle Marche
Classificazione: 8 Tesi di dottorato
Abstract: Il Facility Management in edifici complessi richiede una grande quantità di informazioni che possono essere archiviate in un modello funzionale dell’edificio. Un modello funzionale è una rappresentazione strutturata dell'edificio che include informazioni cruciali per funzioni specifiche come la sicurezza, le azioni di ristrutturazione o il funzionamento e la manutenzione. Il rilevamento di questo tipo di dati, come le proprietà tecniche dei componenti dell'edificio, è un processo costoso. Per questo motivo, è necessario uno strumento avanzato il rilievo ingegneristico. Oggi molti studi si concentrano ancora sull'acquisizione della geometria, trascurando il fatto che molte azioni ricorrenti sono condotte su componenti all'interno degli edifici. Molti sistemi proposti sfruttano tecniche di rilevamento altamente accurate, come la scansione laser o la fotogrammetria, ma che richiedono lunghi sforzi di post-elaborazione per interpretare i dati raccolti. Inoltre, queste operazioni non vengono eseguite sul posto, portando a imprecisioni causate da un’interpretazione errata dei dati. In queste circostanze, la possibilità di eseguire la maggior parte delle operazioni in loco renderebbe sicuramente il processo più efficiente e ridurrebbe gli errori. Questa ricerca propone un sistema di digitalizzazione che sfrutta la collaborazione uomo-macchina evitando fasi di post-elaborazione del dato. A questo scopo, vengono sfruttate le potenzialità della Mixed Reality quali la sua capacità di interagire con il mondo reale, creando un ambiente ideale per la collaborazione uomo-macchina. La capacità della Mixed Reality di sovrapporre i dati digitali all'ambiente reale rende possibile il controllo dei dati direttamente in sito. Per il processo di riconoscimento degli oggetti il sistema proposto in questa ricerca si avvale di rete neurale. La rete neurale YOLO (You Only Look Once) è stata scelta per la sua velocità e funzionalità di rilevamento multiplo, ideale per applicazioni in tempo reale. Il sistema è stato sviluppato e le sue prestazioni sono state valutate per il rilevamento di componenti del sistema antincendio. Il primo set di allenamenti è stato testato ed ha raggiunto sempre più dell'85% del fattore F1. Quindi l'intero sistema è stato testato in sito per dimostrare la sua fattibilità in uno scenario del mondo reale.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/274488 Collegamento a IRIS

2020
Development of a Twin Model for Real-time Detection of Fall Hazards
Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020)
Autore/i: Messi, L.; Corneli, A.; Vaccarini, M.; Carbonari, A.
Editore: International Association on Automation and Robotics in Construction
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The Architecture, Engineering and Construction (AEC) industry is still one of the most hazardous industries in the world. Researchers impute this trend to many factors such as the separation between the phases of safety planning and project execution, implicit safety issues and, most of all, the dynamic and complex nature of construction projects. Several studies show that the AEC industry could greatly benefit of latest advances in Information and Communication Technologies (ICTs) to develop tools contributing to safety management. A digital twin of the construction site, which is automatically instantiated and updated by real-time collected data, can run fast forward simulations in order to pro-actively support activities and forecast dangerous scenarios. In this paper, the twin model of the Digital Construction Capability Centre (DC3) at the Polytechnic University of Marche (UNIVPM) is developed and run as a mock-up, thanks to the adoption of a serious game engine. This mock-up is able to mirror all the relevant features of a job site during the execution of works from a safety-wide perspective. In such a scenario, virtual avatars randomly explore the construction site in order to detect accessible, unprotected and risky workspaces at height, while warning the safety inspector in case additional safety measures are needed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290356 Collegamento a IRIS

2020
BPMN 2.0 Modelling for the Management of the Inspection of Execution Processes in Construction
Proceedings of the Creative Construction e-Conference (2020)
Autore/i: Gardini, Alice; Alvise Bragadin, Marco; Naticchia, Berardo; Carbonari, Alessandro; Corneli, Alessandra
Editore: Attila Varga, Róbert Hohol, Gergely Szakáts
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Nowadays digitalization is a growing challenge for the whole construction sector. Therefore, the need of supportive tools and procedures is becoming increasingly urgent in each construction project step and particularly for project supervision in the execution phase. This is a primary requirement especially for the public sector, since the legislative framework is becoming more and more focused on this aspect, in Italy as well as in the European and international context. A formalization of the inspection procedures of project management in the construction phase is proposed through Business Process Modelling and Notation (BPMN) 2.0 language. The first key aspect of this proposal is to assume a model-based approach, which allows a more coherent information management, in contrast with the traditional document-based one. The second aspect concerns process modeling. In fact, the proposed method is based on processes, instead of BIM – based federated object-oriented models. Construction processes are modelled so that they can generate and feed the federated models themselves. In addition to this, Business Process Modelling and Notation can be used to create a collection of different procedures involved in the inspection management for construction projects. Also, BPMN model will allow an automatic feeding of an inspection management support system which will be developed in future studies, that will offer the full traceability of the procedures and the delivery of the quality certification of products. The case study of the inspection of ready-mix concrete cast-in-place process is analyzed and discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290355 Collegamento a IRIS

2020
Training of YOLO Neural Network for the Detection of Fire Emergency Assets
Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC 2020)
Autore/i: Corneli, A.; Naticchia, B.; Vaccarini, M.; Bosché, F.; Carbonari, A.
Editore: International Association on Automation and Robotics in Construction
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: 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.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290357 Collegamento a IRIS

2020
Framework based on building information modeling, mixed reality, and a cloud platform to support information flow in facility management
FRONTIERS OF ENGINEERING MANAGEMENT
Autore/i: Naticchia, Berardo; Corneli, Alessandra; Carbonari, Alessandro
Classificazione: 1 Contributo su Rivista
Abstract: The quality of information flow management has a remarkable effect on the entire life cycle of buildings. Manual retrieval of technical specifications and features of building components and their performance assessment leads to increased cost and time and efficiency reduction, especially during the facility management (FM) stage. The introduction of building information modeling (BIM) in the construction industry can provide a valuable means of improving the organization and exchange of information. BIM tools integrate multiple levels of information within a single digital model of a building. Nevertheless, the support given by BIM to FM is far from being fully effective. Technicians can benefit from real-time communication with the data repository whenever the need for gathering contextual information and/or updating any data in the digital model arises. The framework proposed in this study aims to develop a system that supports on-site operations. Information requirements have been determined from the analyses of procedures that are usually implemented in the building life cycle. These studies set the standard for the development of a digital model of a building, which will be shared among various actors in charge of FM and accessed via a cloud platform. Moreover, mixed reality is proposed to support specific information that is relevant to geometric features and procedures to be followed by operators. This article presents three use-cases supported by the proposed framework. In addition, this research article describes the first proof of concept regarding real-time support for FM.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/290353 Collegamento a IRIS

2019
Augmented Reality and Deep Learning towards the Management of Secondary Building Assets
Proceedings of 36th International Symposium on Automation and Robotics in Construction
Autore/i: Corneli, A.; Naticchia, B.; Carbonari, A.; Bosche, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The retrieval of as-is information for existing buildings is a prerequisite for effectively operating facilities, through the creation or updating of Building/Asset Information Models (BIM/AIM), or Digital Twins. At present, many studies focus on the capture of geometry for the modelling of primary components, overlooking the fact that many recurring actions need to be conducted on assets inside buildings. Furthermore, highly accurate survey techniques like laser scanning need long offsite processing for object recognition. Performing such process on site would dramatically impact efficiency and also prevent the need to revisit the site in the case of insufficient/incomplete data. In this paper, an Augmented Reality (AR) system is proposed enabling inventory, information retrieval and information update directly on-site. It would reduce post-processing work and avoid loss of information and unreliability of data. The system has a Head-Mounted Display (HMD) AR interface that lets the technician interact handsfree with the real world and digital information contained in the BIM/AIM. A trained Deep Learning Neural Network operates the automatic recognition of objects in the field of view of the user and their placement into the digital BIM. In this paper, two uses cases are described: one is the inventory of small assets inside buildings to populate a BIM/AIM, and the second is the retrieval of relevant information from the AIM to support maintenance operations. Partial development and feasibility tests of the first use case applied to fire extinguishers, have been carried out to assess the feasibility and value of this system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/271351 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

2018
Mixed Reality Approach for the Management of Building Maintenance and Operation
35th International Symposium on Automation and Robotics in Construction
Autore/i: Naticchia, Berardo; Corneli, Alessandra; Carbonari, Alessandro; Bonci, Andrea; Pirani, Massimiliano
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Building Information Modelling (BIM) has been indicated as the right tool to provide the construction industry with the productivity boost that has been lacking in the last 40 years. This momentum finds its highest fulfilment in the support provided by BIM models to knowledge management and the automation of process. However, the management of information flow is still far from its automation breakthrough and, as far as operation and maintenance are concerned, knowledge about procedures is often incomplete. Moreover, processes are often do not undergo optimisation and the individual steps to be performed are not communicated, but in facility management (FM) the transmission of know-how is fundamental particularly because some operations are dangerous or risky. In this framework, Mixed Reality (MR) represents a powerful means to communicate the correct data concerning both geometric features to be considered and the standard procedure to be followed. The aim of this paper is to investigate the possibility of exploiting the benefits provided by these technologies to achieve automation in the transmission of information and optimisation of procedures, improving efficiency and productivity thanks to a better understanding of the operations to be carried out and the reduction of errors. With the support of a head-mounted display (HDM) device with a see-through screen capable of presenting 3D virtual objects, this research tries to combine information from a BIM model with reality to study the benefits for maintenance personnel.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/263452 Collegamento a IRIS

2018
Development of a framework to support the information flow for the management of building
2018 Creative Construction Conference
Autore/i: Naticchia, Berardo; Corneli, Alessandra; Carbonari, Alessandro
Editore: Miklós Hajdu and Mirosław J. Skibniewski
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Inefficient control of information flow in projects is one of the critical aspects that affects the entire lifecycle of buildings. Besides allowing for a simpler and more efficient transfer of information, the dramatic growth of the digitalization process in the AEC industry underlines the need for a common data environment, which manages and shares these data. The increasingly widespread adoption of Building Information Modeling (BIM) is partially leading to a union of multiple levels of information in a single digital model of the building. However, many challenges are still posed in terms of information transfer from the model to operators responsible for keeping building functioning and in good conditions. In fact, technicians could benefit from the immediate availability of data on the current state of buildings and from the level of information detail that can be obtained from digital buildings. The purpose of this work is to create a framework for data management related to the maintenance phase of the building asset. Starting from the study of maintenance processes it was possible to define the information needs that will be managed by a common data environment support associated with BIM models of buildings. Furthermore, thanks to the aid of Mixed Reality (MR), the flow of information is transferred directly to the last user both as regards geometric features and for the standard procedure to be followed. This will allow a maximum optimization of data management procedures due to an automation of processes that will result in a lower incidence of errors in the processes leading eventually to an increase in quality and productivity.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/263459 Collegamento a IRIS

2017
Decision Support Tool for Multi-Criteria Analyses of the Quality of Large Building Stock
Proceedings of ISARC 2017
Autore/i: Carbonari, A.; Giretti, A.; Corneli, A.; Villa, V.; Di Giuda, G.
Editore: Tribun EU, s.r.o., Brno, 2017
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The management of any large building stock with limited resources poses a problem of prioritization of refurbishment actions. Also, available technical information about the building stock is often incomplete and the process of standardization and updating is expensive and time consuming. Some public owners are developing preliminary BIM models of their stock, but they are willing to limit the complexity of the models within the lowest amount of information required for management and maintenance, so as to make that process affordable. Indeed, administrations are challenged by their duty relative to planning regular maintenance and operation of buildings, because of the legislation in force, which requires monitoring of their facilities. For the reasons stated above, this paper presents a decision support tool that can help prioritize refurbishment actions on large building assets. To this purpose, many requirements must be jointly considered in this examination, each requirement being assessed by means of one or several indicators. Then the indicators are compared one another, according to a multi-criteria approach, that weighs the several criteria and rank the assets. In order to deal with the extensive and uncertain information that must be managed in this process, indicators are estimated by means of Bayesian Networks. This tool is used first to assess the technical indicators and rank the assets, while marking any facilities not complying with regulations. Then, additional Bayesian Networks are in charge of estimating the budget needed to upgrade non-compliant facilities with minimum legislation requirements. The outcomes of this research can be used even to assess the level of detail of the information that must be included in BIM models of the stock, in fact acting as guidelines for their development. Finally, the application of the decision tool on a real test case will be presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254086 Collegamento a IRIS

2017
A decision support system for the multicriteria analysis of existing stock
PROCEDIA ENGINEERING
Autore/i: Corneli, A.; MESCHINI, SILVIA; Villa, V.; Di Giuda, G. M.; Carbonari, A.
Classificazione: 1 Contributo su Rivista
Abstract: Owners of any large building stock, such as public administrations, usually have to manage a huge variety of buildings with a limited budget. For this reason, targeted refurbishing actions are needed to ensure that those buildings comply with the latest standards, to preserve the stock and also keep it in good condition. As a result, most public administrators have to make important decisions regarding what part of their stock should be refurbished first. In this paper a new methodology regarding an objective assessment of the quality of large building stock is suggested, as it could help prioritize refurbishment actions. The methodology is based on a decision support system, that is capable of semiautomatically evaluating the compliance of existing buildings with a set of rules by means of the application of Bayesian Networks. The main findings of this research led to the identification of relevant parameters to be used for that assessment; the re-use of those parameters to build a multi-criteria analysis tool; the identification of criteria and requirements to interface this decision tool with BIM models of the stock under consideration. A rough estimation of costs needed to refurbish those buildings that are not compliant, in order to include budget concerns, will be dealt with in the next research step. Finally, a preliminary application of the decision support system to evaluate two Italian school buildings – selected as case studies - will be reported.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/254085 Collegamento a IRIS




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