Publication of an international book by a faculty member of University off Zabol in Springer Publishing House.

A book titled Structural Reliability Analysis (Analytical Methods) was published by a faculty member of Zabol University and China University in the prestigious international Springer Publishing House.
This book is the result of 8 years of research on "reliability" by Behrouz Kashtgar and Professor Shun-Peng Zhu, which has been published in Springer's Structural Integrity Journal, Issue 31, ISBN 978-3-031-75197-4. The authors of this book are Dr. Behrooz Keshtehgar, a faculty member of the Department of Civil Engineering, Zabol University, whose research interests include structural reliability analysis, nonparametric regression, probabilistic analysis, and reliability-based optimization. He also focuses on machine learning methods and mathematical modeling. His work emphasizes stable control approaches, conjugate gradient-based sensitivity analysis, and reliability analytical search techniques. Dr. Shun-Peng Zhu is a professor of mechanical engineering at the University of Science and Technology of China. His research focuses on fatigue design, structural reliability analysis, probabilistic failure physics, multiphase damage modeling, and life prediction of structures and engineering materials.
This book is a scholarly textbook on reliability analysis methods in modern structural design. It is organized in eight comprehensive chapters, starting with basic methods and key definitions, and then moving on to first-order reliability formulation such as the HL-RF method and maximum slope iterative methods, focusing on convergence control and stability enhancement of maximum slope iterative methods, extended gradient methods, and conjugate-directional gradient formulations, as well as the probabilistic model of maximum likelihood search (MPP). The book addresses challenges such as instability control and search extension adjustment in the MPP iterative process. A new optimization model for searching for the most probable point (MPP) in the principal space for interval-based random variables is introduced, which provides an advanced approach to uncertainty optimization. The book concludes with MPP-based reliability analysis methods and forward-looking discussions of emerging analytical perspectives in this field. For more information, see this link.