Changdi He, a Ph.D. candidate in mining engineering at the University of Utah, is revolutionizing how rock strength is analyzed using image processing, numerical modeling, and machine learning. His research introduces a novel, AI-driven approach to determining rock strength, which could enhance efficiency in mining and geological studies while also finding applications in planetary exploration.
Traditionally, mechanical testing has been the standard method for evaluating rock strength, requiring extensive time, effort, and specialized equipment. He’s innovation shifts this paradigm by using high-resolution images of rock samples to train machine learning models that predict their strength. This non-destructive, data-driven method reduces the need for mechanical testing, making rock assessments faster and more cost-effective.
Beyond mining, He’s research will extend to hyperspectral imaging technology, a crucial tool in both terrestrial and space-based geological studies. Hyperspectral cameras capture a broad spectrum of data, allowing for detailed analysis of mineral composition. Integrating He’s machine learning models with these cameras enables automated rock strength assessments not only on Earth but also in extraterrestrial environments such as the Moon and Mars, where traditional mechanical testing is impractical.
By bridging artificial intelligence with mining and planetary sciences, He’s work is transforming how geologists and engineers approach rock mechanics. His research could lead to smarter resource exploration, more efficient mining operations, and enhanced geological surveying for space missions.
“This technology has the potential to streamline rock strength assessments in ways never seen before. By leveraging AI, we can reduce testing times, optimize resources, and even apply these models to space exploration,” He said.
With his pioneering research, He is shaping the future of mining engineering, unlocking new frontiers in geomechanics, and contributing to advancements in space exploration.
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