Introduction

The international journal of Computer-Aided Civil and Infrastructure Engineering (CACAIE) is a rigorously peer-reviewed research journal, published 12 times per year and devoted to publication of original research articles describing novel computational algorithms and innovative applications of computers in civil and infrastructure engineering. This special issue of the CACAIE journal is devoted to recent research in computational advances in damage assessment based on vision techniques and machine learning, and system identification supported by advanced analysis and Bayesian learning. It follows eight other special issues of the journal on the same topic that were published as issues 16:1, 2001, 21:4, 2006, 23:5, 2008, 26:3, 2011, 28:3, 2013, 29:9, 2014, 30:8, 2015, and 33:1, 2018. In response to a call for articles for the current special issue, 24 articles were submitted for possible publication. Each article was rigorously reviewed anonymously for quality and originality by 5–10 reviewers. The review process for the article coauthored by a Guest Editor was handled independently by the journal’s Editor-in-Chief. Six articles from four different countries that met the high standards of the journal were finally approved for publication in the special issue after undergoing two full rounds of reviews, resulting in an acceptance rate of 25%. We sincerely thank the many reviewers of the submitted articles for their in-depth reviews and constructive contributions. Furthermore, we thank all the authors of the submitted articles for their interest in the special issue. Finally, we are also grateful to the Editor-in-Chief, Prof. Hojjat Adeli, for his encouragement, assistance, and support in producing this special issue.