Dear Colleagues,

Nowadays, one of the most fascinating areas of computing is machine learning (ML). In recent decades, ML has effectively been applied to address real-world issues and has established itself as a fixture of daily life. Machine learning has been utilized in a wide range of applications in a variety of fields, including engineering, business, industry, finance, and medicine. ML includes a wide range of learning techniques, from traditional ones such as linear regression, k-nearest neighbors, or decision trees, to support vector machines and neural networks, to recently developed ones such as deep learning and boosted tree models. In reality, it might be difficult to select the appropriate architecture and ML model parameters to develop a learner model that performs well for both generalization and learning. Dealing with large, missing, distorted, and unclear amounts of data is one of the additional challenges that practical ML applications face. Further, challenges in ML are extended beyond suitable architectural selection and data handling, and include, but are not limited to, interpretability, predictive analytics, data reasoning, and edge computing; hence, the whole research community would benefit from innovative research perspectives highlighting and overcoming new challenges in ML for engineering applications (especially those focusing on smart construction and sustainable development).

Moreover, the term "computer-aided design and analysis" refers to the use of computer technology to facilitate product design, analysis, and manufacturing. Due to the rapid advancement of science and technology, significant progress has been accomplished in this domain. The advantages of computer-aided design and analysis are enormous; they include shorter lead times, more effective designs, astute risk management, and economical production. Despite significant advancements, there is still much work needed to be carried out on a variety of challenges in the field of computer-aided design and analysis for engineering applications, including, but not limited to, the synthesis, optimization, and representation of design specifications; the use of large databases; and the role of software engineering tools.

This Special Issue emphasizes the usage of ML models across several engineering domains and issues. Papers are expected to present significant findings on a variety of learning techniques and discuss how problems are conceptualized, how data is represented, how features are developed, how machine learning models are used, how they are compared to other methods, and how the results are interpreted. Recent ML innovations such as deep learning and boosted tree models should receive more attention. This Special Issue also encourages papers revealing original research, as well as novel or particularly remarkable computer-aided designs and analyses for engineering applications. These themes include all phases of design, from idea to production and beyond. Researchers from all disciplines and application areas are encouraged to submit contributions as long as they contain significant geometric, topological, spatial, structural, or configuration design content and highlight new developments that will likely be of interest to a wide range of researchers, educators, and practitioners. This Special Issue focuses on the integration of cutting-edge and developing computer and information technologies for creative engineering problem solving. This Special Issue encourages multidisciplinary research and offers a distinctive venue for creative computer-aided engineering to promote new computational paradigms. The following topics are covered by this Special Issue (but you are not limited to only these): cognitive modeling, database management, concurrent engineering, evolutionary computing, fuzzy distributed computing, genetic algorithms, logic, Internet-based technologies, intelligent and adaptive systems, machine learning, computer-aided design, new computational methods, computer and smart construction, optimization, computer-aided analysis, finite element modeling, discrete element modeling, constitutive modeling, numerical modeling, analytical modeling, and spatial modeling.

Dr. Zia Ur Rehman
Dr. Nauman Ijaz
Dr. Usama Khalid
Dr. Sadaqat Ur Rehman
Guest Editors

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• machine learning/artificial intelligence
• computer-aided design and analysis
• computational methods and programming
• deep learning/architecture/theory
• numerical methods/simulations
• digital manufacturing/smart construction
• computers and engineering
• optimization
• computers and statistics
• constitutive modeling