Development of a BIM-based simulator for workspace management in construction

openNei cantieri edili, quali contesti altamente dinamici, lo spazio richiesto dalle attività muta continuamente evidenziando la necessità di considerarlo come una risorsa limitata. Ad oggi, le aree di lavoro non sono efficacemente gestite né dalle tecniche tradizionali di pianificazione né dagli strumenti 4D più avanzati. I manager del processo costruttivo sono costretti a condurre considerazioni spaziali manualmente sulla base di schizzi 2D. Tale approccio è altamente dispendioso e soggetto ad errori; inoltre, come dimostrano le statistiche, è una delle principali cause di infortuni e riduzione della produttività. Questa tesi di dottorato affronta il problema della gestione delle aree di lavoro, proponendo un approccio che integra la fase di pianificazione con la verifica delle interferenze, condotta da un simulatore spaziale sviluppato in un motore grafico. Tale simulatore, acquisiti il modello BIM ed il cronoprogramma, individua eventuali conflitti spaziali quale risultato di computazioni geometriche e simulazioni fisiche. La criticità dei conflitti viene stimata mediante inferenza Bayesiana al fine di escludere scenari trascurabili. Successivamente, i manager del processo costruttivo, consapevoli dei possibili conflitti spaziali futuri, modificano o confermano il cronoprogramma. Questo approccio può essere applicato per identificare i conflitti spaziali sia durante la fase di pianificazione che quella di esecuzione dei lavori. In questa tesi, il simulatore spaziale proposto è stato validato con riferimento alla fase di pianificazione dei lavori di un edificio reale. I risultati hanno dimostrato la sua capacità di identificare non solo un maggior numero di conflitti, rispetto agli strumenti dello stato dell’arte, ma anche di stimare il relativo livello di criticità evitando sovrastime. In futuro, l’approccio proposto in questa tesi, adattato con minime integrazioni, potrà essere applicato a runtime per aggiornare il cronoprogramma durante l’esecuzione dei lavori.In the AEC industry, construction sites are very dynamic operating environments. Activities workspace demand continuously changes across space demanding and time, stressing the need to consider the space as a limited and renewable resource. This issue has not been fully handled yet, neither by traditional scheduling techniques nor by more advanced 4D tools. For these reasons, construction management teams usually carry out manually spatial considerations based on 2D sketches. This approach, especially in big construction projects, is highly time-demanding and error-prone causing, as demonstrated by statistics, injuries, and productivity slowdown. To cover these gaps, this study proposes a workspace management framework that integrates the work scheduling phase with spatial analysis, carried out by a spatial conflict simulator developed using a serious game engine. The simulator, given the BIM model and the construction work schedule, can detect eventual spatial interferences based on geometric computations and physics simulations. The detected conflicts are then judged applying Bayesian inference to filter non-critical scenarios and avoid overestimation. Afterwards, the construction management team, made aware of likely future spatial issues, can adjust or confirm the work schedule. This approach can provide a valuable contribution in detecting spatial conflicts during both the construction planning phase and works execution. In this study, the proposed spatial conflict simulator has been validated on the planning phase of a real use case, demonstrating its capability to not only detect an increased number of spatial issues, compared to the state-of-the-art tools, but also to esteem related criticality levels and avoid overestimations. In the future, the proposed approach, adapted with minor changes, can be applied at runtime for proactively refining the work schedule during works execution.INGEGNERIA CIVILE, AMBIENTALE, EDILE E ARCHITETTURAopenMessi, Leonard

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.

To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival. We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients' disease trajectories and predict the probability of functional impairment and survival at different time points. DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80-0.93 and 0.84-0.89 for the two scenarios, respectively). Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making. [Abstract copyright: © 2022. The Author(s).

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic