Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

A Knowledge Based Educational (KBEd) framework for enhancing practical skills in engineering distance learners through an augmented reality environment

The technology advancement has changed distance learning teaching and learning approaches, for example, virtual laboratories are increasingly used to deliver engineering courses. These advancements enhance the distance learners practical experience of engineering courses. While most of these efforts emphasise the importance of the technology, few have sought to understand the techniques for capturing, modelling and automating the on-campus laboratory tutors’ knowledge. The lack of automation of tutors’ knowledge has also affected the practical learning outcomes of engineering distance learners. Hence, there is a need to explore further on how to integrate the tutor's knowledge, which is necessary for imparting and assessing practical skills through current technological advances in distance learning. One approach to address this concern is through the use of Knowledge Based Engineering (KBE) principles. These KBE principles facilitate the utilisation of standardised methods for capturing, modelling and embedding experts’ knowledge into engineering design applications for the automation of product design. Hence, utilising such principles could facilitate, automating engineering laboratory tutors’ knowledge for teaching and assessing practical skills. However, there is limited research in the application of KBE principles in the educational domain. Therefore, this research explores the use of KBE principles to automate instructional design in engineering distance learning technologies. As a result, a Knowledge Based Educational (KBEd) framework that facilitates the capturing, modelling and automating on-campus tutors’ knowledge and introduces it to distance learning and teaching approaches. This study used a four-stage experimental approach, which involved rapid prototyping method to design and develop the proposed KBEd framework to a functional prototype. The developed prototype was further refined through internal and external expert group using face validity methods such as questionnaire, observation and discussion. The refined prototype was then evaluated through welding task use-case. The use cases were assessed by first year engineering undergraduate students with no prior experience of welding from Birmingham City University. The participants were randomly separated into two groups (N = 46). One group learned and practised basic welding in the proposed KBEd system, while the other learned and practised in the conventional on-campus environment. A concurrent validity assessment was used in determining the usefulness of the proposed system in learning hands-on practical engineering skills through proposed KBEd system. The results of the evaluation indicate that students who trained with the proposed KBEd system successfully gained the practical skills equivalent to those in the real laboratory environment. Although there was little performance variation between the two groups, it was rooted in the limitations of the system’s hardware. The learning outcomes achieved also demonstrated the successful application of KBE principles in capturing, modelling and transforming the knowledge from the real tutor to the AI tutor for automating the teaching and assessing of the practical skills for distance learners. Further the data analysis has shown the potential of KBEd to be extendable to other taught distance-learning courses involving practical skills