Ding Ding

Ka-Veng Yuen

Ping Xiang

Shoulong Zhang

This open access volume presents select proceedings of 2025 International Conference on Engineering Management and Safety Engineering (EMSE 2025) will be held on 21-23 March 2025 in Nanjing, China. With a focus on the latest challenges and opportunities facing engineering management and safety engineering today, the conference aims to bring together scholars, industry experts, and policy makers from home and abroad to discuss and exchange advanced theoretical research, best practices, and emerging technologies. Being an interdisciplinary platform, the conference will cover multiple dimensions of engineering management, including project planning, implementation, compliance, and post-assessment. Safety engineering topics will also be highlighted, involving safety management, risk assessment, case studies and implementation of safety regulations and standards, and the effectiveness of safety training. This international academic conference is expected to provide new perspectives and references for the development of project management and safety engineering, and at the same time to promote the close cooperation between academia and practice, contributing wisdom and motivation for the enhancement and innovation of the industry.

Proceedings of the 2025 International Conference on Engineering Management and Safety Engineering (EMSE 2025)

Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily pollutants generated by cars and factories within the area of concern. It is also difficult to obtain the updated air quality information of the surrounding cities. Furthermore, the meteorological conditions of the day for prediction are also uncertain. These are just some of the civil engineering examples to which Bayesian probabilistic methods are applicable. Familiarizes readers with the latest developments in the field Includes identification problems for both dynamic and static systems Addresses challenging civil engineering problems such as modal/model updating Presents methods applicable to mechanical and aerospace engineering Gives engineers and engineering students a concrete sense of implementation Covers real-world case studies in civil engineering and beyond, such as: structural health monitoring seismic attenuation finite-element model updating hydraulic jump artificial neural network for damage detection air quality prediction Includes other insightful daily-life examples Companion website with MATLAB code downloads for independent practice Written by a leading expert in the use of Bayesian methods for civil engineering problems This book is ideal for researchers and graduate students in civil and mechanical engineering or applied probability and statistics. Practicing engineers interested in the application of statistical methods to solve engineering problems will also find this to be a valuable text. MATLAB code and lecture materials for instructors available at http://www.wiley.com/go/yuen

Bayesian Methods for Structural Dynamics and Civil Engineering

Wan-Huan Zhou

Zhen-Yu Yin

This book introduces systematically the application of Bayesian probabilistic approach in soil mechanics and geotechnical engineering. Four typical problems are analyzed by using Bayesian probabilistic approach, i.e., to model the effect of initial void ratio on the soil–water characteristic curve (SWCC) of unsaturated soil, to select the optimal model for the prediction of the creep behavior of soft soil under one-dimensional straining, to identify model parameters of soils and to select constitutive model of soils considering critical state concept. This book selects the simple and easy-to-understand Bayesian probabilistic algorithm, so that readers can master the Bayesian method to analyze and solve the problem in a short time. In addition, this book provides MATLAB codes for various algorithms and source codes for constitutive models so that readers can directly analyze and practice. This book is useful as a postgraduate textbook for civil engineering, hydraulic engineering, transportation, railway, engineering geology and other majors in colleges and universities, and as an elective course for senior undergraduates. It is also useful as a reference for relevant professional scientific researchers and engineers.

Ka-Veng Yuen

Books

Your next favorite book is out there