Structural Health Monitoring. A Machine Learning Perspective👓 Worden Keith
Structural Health Monitoring. A Machine Learning Perspective
✅ Written 🖋️ by global leaders and ➕ pioneers in the field 🏑, this book 📚️ is a must-have read 👤📖 for researchers, practicing engineers 👨🔬 and ➕ university faculty working ⚙️ in SHM. Structural Health Monitoring: A Machine Learning Perspective is the first 🥇 comprehensive book 📚️ on the general problem of structural health monitoring. The authors, renowned experts in the field 🏑, consider structural health monitoring in a new 🆕 manner by casting the problem in the context of a machine learningstatistical pattern recognition paradigm, first 🥇 explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and ➕ experimental 🥼 studies of laboratory test specimens and ➕ in-situ structures. This paradigm provides a comprehensive framework for developing 👨💻️ SHM solutions. Structural Health Monitoring: A Machine Learning Perspective makes 🛠️ extensive use of the authors’ detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and ➕ the results of performing 🎭️ numerous analytical and ➕ experimental 🥼 structural health monitoring studies. Considers structural health monitoring in a new 🆕 manner by casting the problem in the context of a machine learningstatistical pattern recognition paradigm Emphasises an integrated approach to the development 👨💻️ of structural health monitoring solutions by coupling 👩❤️💋👨 the measurement hardware portion of the problem directly with the data interrogation algorithms Benefits from extensive use of the authors’ detailed surveys of 8️⃣0️⃣0️⃣ papers 📜 in the technical literature and ➕ the experience they have gained from teaching numerous short courses on this subject.
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