Η παρούσα διδακτορική διατριβή πραγματεύεται τη διερεύνηση της σχέσης της τεχνικογεωλογικής δομής της βραχομάζας των ασβεστολίθων (εκρηκτική ικανότητα, αντοχή, δομή, ποιότητα της βραχομάζας, κατάσταση των ασυνεχειών), του εκάστοτε μετώπου εξόρυξης, σε λατομεία παραγωγής αδρανών υλικών, με την κοκκομετρία του υλικού που προκύπτει μετά την ανατίναξη, συνυπολογίζοντας την ποσότητα της εκρηκτική ύλης τύπου ANFO που εφαρμόζεται. Η κοκκομετρία του εξορυσσόμενου υλικού αποτελεί έναν πολύ σημαντικό παράγοντα στα λατομεία αδρανών, καθώς αυτή αποτελεί έναν δείκτη της αποτελεσματικότητας της ανατίναξης. Ένα αποδεκτό αποτέλεσμα, θεωρείται όταν τα τεμάχη που προκύπτουν δεν είναι απαραίτητο να υποστούν δευτερογενή θραύση με τη χρήση υδραυλικού σφυριού και επιπλέον υπάρχει μικρό ποσοστό λεπτόκοκκου υλικού στην εξορυσσόμενη σωρό. Το μέγεθος των τεμαχών εξαρτάται από παραμέτρους που διέπουν τόσο τα χαρακτηριστικά του ασβεστολίθου στο εξορυσσόμενο μέτωπο όσο και από παραμέτρους που συνθέτουν τη διαδικασία της ανατίναξης. Η παρούσα έρευνα εστιάζει στην επίδραση των τεχνικογεωλογικών παραγόντων και των παραμέτρων που διέπουν την ανατίναξη, μέσω της στατιστικής ανάλυσης αυτών αλλά και τη δημιουργία εύκολα διαχειρίσιμων νομογραμμάτων για επί τόπου χρήση στο πεδίο και αυτοματοποιημένων μοντέλων (ΑΝΝ).  
Ειδικότερα, το σύνολο των δεδομένων (100 καταγραφές, από 50 για κάθε λατομείο) συλλέχθηκε από δύο λατομεία εξόρυξης αδρανών υλικών της εταιρίας Ιντερμπετόν του ομίλου ΤΙΤΑΝ, το λατομείο του Δρυμού και των Ταγαράδων στην Κεντρική Μακεδονία, πλησίον της πόλης της Θεσσαλονίκης, όπου οι υπόλοιπες παράμετροι σύνθεσης των ανατινάξεων, παραμένουν σχεδόν σταθερές (κάναβος διάταξης διατρημάτων, διάμετρος και βάθος διατρημάτων κ.α.). Ο δείκτης εκρηκτικής ικανότητας αποτελεί μία πολύ σημαντική παράμετρο εκτίμησης της ευκολίας ή της δυσκολία εκσκαφής της βραχομάζας με τη χρήση εκρηκτικών υλών, υπό συγκεκριμένες συνθήκες σχεδιασμού της ανατίναξης, λαμβάνοντας υπόψη την κατανάλωση των εκρηκτικών, (Latham and Lu 1999). Η εκρηκτική ικανότητα (ΒΙ) εξαρτάται από τη δομή της βραχομάζας, την απόσταση και τον προσανατολισμό των ασυνεχειών, το ειδικό βάρος του πετρώματος και την αντοχή σε μονοαξονική θλίψη (Lilly 1986, 1992). Στην παρούσα διατριβή η δομή αποδόθηκε μέσω του Γεωλογικού δείκτη αντοχής (GSI) (Marinos et al. 2005), προκειμένου να εκτιμηθεί με μεγαλύτερη ακρίβεια η κατάσταση της βραχομάζας, οδηγώντας σε μία πιο ακριβή ταξινόμηση της εκρηκτικής ικανότητας ορίζοντας τον τροποποιημένο Δείκτη εκρηκτικής ικανότητας (ΜΒΙ), (Μodified Βlastability Ιndex).   
Από την αξιολόγηση των αποτελεσμάτων προέκυψε ότι δύο παράγοντες επιδρούν στο μέγεθος των αποσπώμενων τεμαχών και ο πρώτος αφορά τη συμπεριφορά της βραχομάζας (εκρηκτική ικανότητα και ποιότητα του ασβεστολίθου) ενώ ο δεύτερος την ποσότητα της εκρηκτικής ύλης τύπου ANFO που εφαρμόζεται στην εκάστοτε ανατίναξη. Περιγράφονται από υψηλούς συντελεστές συσχέτισης με την εκρηκτική ικανότητα να παρουσιάζει τον υψηλότερο με r=0.70, την ποιότητα του ασβεστολίθου με r=0.60 και την κατανάλωση της εκρηκτικής ύλης Powder Factor (ANFO kg/m3) με r=-0.67. Παρατηρήθηκε ότι η κατανομή του τροποποιημένου Δείκτη εκρηκτικής ικανότητας στα δεδομένα ακολουθεί σε μεγάλο βαθμό την κατανομή του μέσου και μέγιστου μεγέθους των εξορυσσόμενων τεμαχών. Οι υψηλές τιμές του ΜBI (81) αντιστοιχούν σε μέγεθος τεμαχών που ορίζουν τη μεγαλύτερη σχεδόν μέση τιμή (45 cm), ενώ στην περίπτωση που η εκρηκτική ικανότητα κυμαίνεται σε χαμηλά επίπεδα (περίπου 44 με 52), τότε και το μέσο μέγεθος κυμαίνεται σε μικρές τιμές μεταξύ 18 και 22 cm. Ακόμα και ογκόλιθοι μεγέθους από 1.10m έως 1.33m αντιστοιχούν σε υψηλές τιμές GSI (70-80), όπου η βραχομάζα χαρακτηρίζεται ως τεμαχώδης με καλή ποιότητα ασυνεχειών και αντίστοιχα το ΜBI κυμαίνεται μεταξύ 69 και 80.
Επιπλέον, από τις καταγραφές συμπεραίνεται ότι στην περίπτωση που το GSI του ασβεστολίθου κυμαίνεται σε χαμηλότερα επίπεδα, με υψηλές τιμές αντοχής από 55 MPa έως 70 MPa, το σύνολο των τιμών του ΜBI έχει εύρος μεταξύ 40 και 65. Αντίθετα, στις περιπτώσεις, όπου επικρατούν χαμηλότερες αντοχές (52 με 60 MPa), και υψηλοί δείκτες GSI ,τότε οι τιμές του ΜBI κινούνται σε υψηλά επίπεδα (60 έως 80). Από αυτό γίνεται κατανοητό το γεγονός της  σημαντικής επίδρασης που ασκεί η ποιότητα της βραχομάζας στην εκρηκτική ικανότητα, έναντι της αντοχής της.     
Τέλος, δημιουργήθηκαν νομογράμματα εύρεσης της μίας παραμέτρου σε σχέση με την άλλη, για επί τόπου χρήση στο πεδίο, που εκκινούν από την ταξινόμηση του ασβεστολίθου στο μέτωπο της εξόρυξης μέσω του GSI και καταλήγουν στους παράγοντες που περιγράφουν την ανατίναξη (κατανάλωση ANFO, αριθμός διατρημάτων, ποσότητα εξορυσσόμενης ύλης, μέσο μέγεθος αποσπώμενων τεμαχών). Παράλληλα με τις συμβατικές μεθόδους, προτείνεται τεχνητό νευρωνικό δίκτυο (ΑΝΝ) πρόβλεψης του μέσου μεγέθους των αποσπώμενων τεμαχών (με δεδομένα εισόδου τον τροποποιημένο Δείκτη εκρηκτικής ικανότητας, την κατανάλωση εκρηκτικής ύλης τύπου ANFO kg/m3, την ποσότητα της εξορυσσόμενης μάζας), με πολλά στρώματα, με δομή 3-5-1, εμπρόσθιας τροφοδότησης (feed-forward net) με επίβλεψη (supervised training), εκπαιδευμένο με τον αλγόριθμο οπισθοδιάδοσης με ορμή (back propagation with momentum) και βήμα εκπαίδευσης 0.5. Το δίκτυο αυτό υποδεικνύει υψηλή στατιστική αξιοπιστία, με ακρίβεια πρόβλεψης τα 2.5 cm και υψηλή υπεροχή έναντι της συμβατικής στατιστικής ανάλυσης (multiple regression analysis), (Dimitraki et al. 2018).       

The present Ph.D. thesis concerns about the investigation between the geotechnical characteristics of the limestone rock mass (blastability, strength, structure, quality, condition of the discontinuities) on pit faces, in aggregates quarries, and the size of the fragments in blasted rock piles, taking into account the quantity of the explosives (ANFO). The ultimate target is to assess the interaction of these parameters on blasted rocks. The size of fragments in blasted rock piles, is an appropriate index of the effectiveness of the blasting process in a pit face. An optimum fragmentation is considered when the fragments do not need to be subjected to secondary breaking (fewer oversize boulders) and the blasted rock pile is described by a small percent of ultra-fines. The fragment size depends on parameters which describe not only the rock mass but also the blasting process (specific charge, spacing, burden, etc.), (Lyana et al. 2016). The present study focuses on these parameters, using the statistical analysis and powerful, advanced computational tools, creating useful nomograms for field implementation.
For the aim of this study, 100 blasting processes were attended at the Drymos and Tagarades quarries in the Central Macedonia region of Greece and recorded for over two years. These two quarries belong to the Titan Company and offer a broad category of coarse to medium grained aggregate materials, including sand, gravels, and crushed stone. At this point, it is worth noting that the blast design (blasthole diameter, burden, space, height of the pit face, sub-drill, detonators), which concerns the blasting process for each quarry, is approximately the same for each blasting event. The BI is a significant mechanical parameter for estimating the vulnerability for the excavation of the rock mass under a specified blast design, by taking into consideration the explosives consumption (Latham and Lu 1999). The BI is related to the rock mass description (RMD), the space (JPS) and the orientation of the joints (JPO), the specific gravity of the rock (SGI) and the uniaxial compressive strength (UCS), through Lilly’s equation (Lilly 1986, 1992). In this equation the RMD is considered as a very wide parameter and needs more precision. Therefore, the geological strength index (GSI) (Marinos et al. 2005) was used, which is more precise for estimating and evaluating the rock mass behavior, leading to a specific classification of the blastability through the Modified Blastability Index (MBI).
After evaluating the results, it is concluded that two factors have an impact on the blasted fragment size. The first one is the rock mass behavior (blastability and quality of the limestone), while the second one concerns about the explosive quantity of ANFO, which is implemented in each blasting process. These factors are described by high correlation coefficients and more specifically the modified blastability index has the highest one with r=0.70, the quality of limestone with r=0.60 and the explosive consumption, Powder Factor (ANFO kg/m3) with r=-0.67. It is noticed that the modified blastability index distribution follows, to a great extent, the distribution of the average and maximum size of the blasted rocks. The high values of MBI (81) correspond to large values of average size (45 cm), while in case of ranging the MBI in low levels (approximately 44-52), and then the average size has lower values between 18 and 22 cm. Even the boulders with size from 1.10 to 1.33m correspond to high values of GSI (70-80), where the rock mass is characterized as blocky with good quality of discontinuities and the MBI ranges between 69 and 80. Furthermore, according to the recordings, it is assumed that in case of the GSI limestone fluctuates in lower levels, with high strength values (55-70 MPa), then the MBI values ranges from 40 to 65. On the contrary, limestone with lower strength (52-60) and high GSI then MBI ranges from 60 to 80. According to these results, it is obvious that the rock mass quality plays a significant role in the MBI contrary to the strength.
Finally, nomograms were created for determining the one parameter in relation to the other, for field implementation, which start from the rock mass classification of the pit face and result in the factors that describe the explosion (ANFO consumption, number of blastholes, quantity of blasted rock, and average size of blasted rock). At the same time, it is proposed an Artificial Neural Network (ΑΝΝ), for predicting the average size of fragments in aggregate blasted rock piles, with three input parameters: the MBI, the explosive consumption Powder Factor (ANFO kg/m3), the quantity of the rock pile. This ANN model is a feed-forward, supervised, multilayer perceptron network, with 3-5-1 structure. The model was trained with back propagation algorithm with momentum and learning rate 0.5. Furthermore, the statistical parameters ensure its efficiency, with prediction accuracy at 2.5 cm. On the other hand, the relationship between dependent and independent variables with the conventional regression analysis is described by unfavorable values of the statistical parameters, (Dimitraki et al. 2018).

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