The mining industry has been proliferating through the past decades, showing a great impact on economies, societies, and industries' development. In particular, underground mining is characterized by harsh environments and thus should be undergone with extreme caution to prevent any intolerable consequences. Therefore, imposing regular and repetitive inspections and monitoring to detect risks, ensure structural stability, and extend tunnel life. Visual inspection has been one of the most desirable and valuable non-destructive methods for providing preliminary information on the structure's condition. However, traditionally this required the physical presence of trained inspectors examining large spans with traditional surveying and monitoring instruments that are time-consuming and error-prone due to human subjectivity. At present, the modern surveying field especially Mobile Mapping Systems is growing and being adopted widely, but handling various forms of 3D data still encounters some challenges that constrain its potential benefits. Accordingly, mining development requires both advancement in surveying technology and the effective utilization of data to ensure efficient productivity and positive environmental performance. This paper proposes an algorithm taking into consideration the needs and requirements of one of the most prestigious mining companies in Italy based on an interview conducted with them to develop a fast, automatic, least time, and effort-consuming optimal workflow where 3D data can assist professionals in studying the tunnels environment. It's capable of (1) skeletonizing the tunnel and describing its geometry and topology by creating a simplified shape descriptor; (2) extracting cross-sections along the central axis; (3) referencing and extracting images at specified locations describing the general scene of the working environment; (4) performing tunnel decomposition and measure various geometric parameters. The results demonstrate that data processing can be automated in an innovative, efficient, and unchallenging manner, allowing mine owners to display, maintain, process, and analyze large amounts of 3D data with the least requirements, increasing their operations' safety, productivity, and sustainability.
L'industria mineraria ha proliferato negli ultimi decenni, mostrando un grande impatto sullo sviluppo delle economie, delle società e delle industrie. In particolare, l'estrazione mineraria sotterranea è caratterizzata da ambienti difficili e quindi dovrebbe essere affrontata con estrema cautela per prevenire qualsiasi conseguenza intollerabile. Pertanto, è necessario imporre ispezioni e monitoraggi regolari e ripetitivi per individuare i rischi, e garantire la stabilità strutturale e prolungare la vita della galleria. L'ispezione visiva è uno dei metodi non distruttivi più desiderabili e preziosi per fornire informazioni preliminari sulle condizioni della struttura. Tuttavia, tradizionalmente questo richiede la presenza fisica di personale addestrato al fine di esaminare grandi campate con strumenti tradizionali di rilevamento e monitoraggio che richiedono molto tempo e sono soggetti a errori a causa della soggettività umana. Attualmente, il campo del rilevamento moderno, specialmente i sistemi di mappatura mobile, sta crescendo e viene adottato ampiamente, ma la gestione di varie forme di dati 3D incontra ancora alcune sfide che limitano i suoi potenziali benefici. Di conseguenza, lo sviluppo minerario richiede sia l'avanzamento della tecnologia di rilevamento che l'utilizzo efficace dei dati per garantire una produttività efficiente. Questa tesi propone un algoritmo che prende in considerazione le esigenze e i requisiti di una delle più prestigiose società minerarie in Italia, sulla base di un'intervista condotta con loro per sviluppare un flusso di lavoro ottimale, veloce e automatico, che richieda meno tempo e sforzi, in cui i dati 3D possano assistere i professionisti nello studio dell'ambiente delle gallerie. Lo strumento proposto è in grado di (1) estrarre lo scheletro dei tunnel sotterranei e descrivere la sua geometria e topologia; (2) estrarre sezioni trasversali lungo l'asse centrale; (3) referenziare ed estrarre immagini in punti specifici che descrivono la scena generale dell'ambiente di lavoro; (4) eseguire la decomposizione del tunnel e misurare vari parametri geometrici. I risultati dimostrano che l'elaborazione dei dati può essere automatizzata in modo innovativo, efficiente e non impegnativo, permettendo ai proprietari di miniere di elaborare, visualizzare, mantenere e analizzare grandi quantità di dati 3D con il minimo dei requisiti, aumentando la sicurezza e la sostenibilità delle loro operazioni.
Algorithm for automatic extraction of information from 3D data for underground mining tunnel models
ELALAILYI, AHMAD
2021/2022
Abstract
The mining industry has been proliferating through the past decades, showing a great impact on economies, societies, and industries' development. In particular, underground mining is characterized by harsh environments and thus should be undergone with extreme caution to prevent any intolerable consequences. Therefore, imposing regular and repetitive inspections and monitoring to detect risks, ensure structural stability, and extend tunnel life. Visual inspection has been one of the most desirable and valuable non-destructive methods for providing preliminary information on the structure's condition. However, traditionally this required the physical presence of trained inspectors examining large spans with traditional surveying and monitoring instruments that are time-consuming and error-prone due to human subjectivity. At present, the modern surveying field especially Mobile Mapping Systems is growing and being adopted widely, but handling various forms of 3D data still encounters some challenges that constrain its potential benefits. Accordingly, mining development requires both advancement in surveying technology and the effective utilization of data to ensure efficient productivity and positive environmental performance. This paper proposes an algorithm taking into consideration the needs and requirements of one of the most prestigious mining companies in Italy based on an interview conducted with them to develop a fast, automatic, least time, and effort-consuming optimal workflow where 3D data can assist professionals in studying the tunnels environment. It's capable of (1) skeletonizing the tunnel and describing its geometry and topology by creating a simplified shape descriptor; (2) extracting cross-sections along the central axis; (3) referencing and extracting images at specified locations describing the general scene of the working environment; (4) performing tunnel decomposition and measure various geometric parameters. The results demonstrate that data processing can be automated in an innovative, efficient, and unchallenging manner, allowing mine owners to display, maintain, process, and analyze large amounts of 3D data with the least requirements, increasing their operations' safety, productivity, and sustainability.| File | Dimensione | Formato | |
|---|---|---|---|
|
2021_4_Elalailyi.pdf
accessibile in internet solo dagli utenti autorizzati
Descrizione: Text of the Thesis
Dimensione
5.84 MB
Formato
Adobe PDF
|
5.84 MB | Adobe PDF | Visualizza/Apri |
I documenti in POLITesi sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/10589/188534