A Review of the State-of-the-Art Optimization Algorithms for Dimensional Stone Cutting
Bibliographic record
Abstract
Abstract In the dimension stone quarrying industry, the presence of fractures and discontinuities within in-situ blocks presents significant challenges, leading to substantial waste production. As a result, the economic viability of ornamental stone quarries relies on the implementation of optimization algorithms to determine ideal cutting directions and block dimensions. These approaches seek to minimize waste generation while maximizing the extraction of non-fractured commercial-sized blocks. This review examines cutting-edge optimization algorithms for dimensional stone cutting through a systematic literature review (SLR) using the PRISMA methodology. The study identified 37 articles from 20 nations, including Italy, Iran, China, Canada, Germany, Croatia, Vietnam, Portugal, Australia, Spain, Austria, USA, Poland, Pakistan, Egypt, Russia, Turkey, Sweden, Japan, and Slovenia, which discussed various optimization algorithms used in the dimensional stone quarry industry. The analysed articles cover a wide range of tools and methods, from ground penetrating radar (GPR) and scanline surveys to photogrammetry and advanced modelling techniques such as 3DEC and Discrete Fracture Networks. Qualitative analysis was conducted using four major databases: Scopus, ScienceDirect, Web of Science, and Springer Nature Link. The findings indicate that all identified optimization algorithms can be categorized into two main groups: those focused on fracture and discontinuity detection and modelling, and those centred on block geometry modelling. In this SLR, GPR technology stands out as a key tool, widely used for its non-destructive nature and effectiveness in identifying fractures and discontinuities within quarry locations. In addition to GPR, stochastic modelling techniques such as discrete fracture networks have also shown effectiveness in simulating fracture networks. Algorithms for block geometry modelling aim to determine the shape, size, and arrangement of quarry blocks. Various algorithms for analyzing block geometry, such as SlabCutOpt, 3D Block Expert, BlockCutOpt, and RANSAC, are commonly combined with fracture modelling tools and algorithms such as DFN and GPR fracture data to improve optimization. This integration is essential for the effective extraction and processing of ornamental stones. The ability to view block boundaries in three dimensions significantly improves decision-making processes, enhances ornamental stone quarry operations, and reduces waste. This SLR offers a comparative framework by examining these different algorithms, allowing for a better understanding of their advantages, constraints, and suitability in diverse ornamental stone-quarrying environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".