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Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. In most instances, successfully solving such problems depends on experts' knowledge and experience. The primary object of this research was to develop an evolutionary fuzzy neural inference system ͑EFNIS͒ to imitate the decision-making processes in the human brain in order to facilitate geotechnical expert decision making. First, an evolutionary fuzzy neural inference model ͑EFNIM͒ was constructed by combining the genetic algorithm ͑GA͒, fuzzy logic ͑FL͒, and neural network ͑NN͒. In the proposed model, GA is primarily concerned with optimizing parameters required in the fuzzy neural network; FL with imprecision and approximate reasoning; and NN with learning and curve fitting. This research then integrates the EFNIM with an object-oriented computer technique to develop an EFNIS. Finally, the potential to apply the proposed system to practical geotechnical decision making is validated using two real problems, namely estimating slurry wall duration and selecting retaining wall construction methods.

The application of virtual reality in a driving simulation is not novel, yet little is known about the use of this technology by senior populations. The effects of age, gender, control device (joystick or handlebar), and task type on wayfinding proficiency using a virtual reality (VR) driving simulation were explored. The driving experiment model involved 96 randomly recruited participants, namely, 48 young people and 48 seniors (split evenly by gender in each group). Experiment results and statistical analyses indicated that, in a VR driving scenario, task type significantly affected VR driving performance. Navigational scores were significantly higher for the straight (easy/symmetrical straight route) task than those for the curved (difficult/asymmetrical curved route) task. The aging effect was the main reason for the significant and interacting effects of gender and control device. Interactions between age and gender difference indicated that the young group exhibited better wayfinding performance than the senior group did, and in the young group, males had better performance than that of females. Similarly, interactions between age and control device indicated that the handlebar control-device type resulted in better performance than the joystick device did in the young group, but no difference was found in the senior group due to age or learning effects. Findings provide an understanding of the evaluation of the interface designs of navigational-support systems, taking into consideration any effects of age, gender, control device, and task type within three-dimensional VR games and driving systems. With a VR driving simulator, seniors can test-drive inaccessible products such as electric bicycles or cars by using a computer at home.

Abstract— This study investigated user task performance in terms of three interface proposals (linear, hierarchical, and network type) and task complexities (single‐layered, double‐layered, and triple‐layered). Forty‐two participants (all male), aged 19–23 years, were recruited for the experiment. The results generated from this study reveal that the different degrees of task complexity interacted with the topological structures of the user interfaces. No difference was found among the three topological structures in the single‐layered task. A linear structure resulted in better user performance than network or hierarchical structures in the double‐layered task, while in the triple‐layered task both network and hierarchical structures resulted in better user performance than a linear topological structure. This study provides an example of the application of topological structures in interface design and evaluation. From a practical perspective, the results imply the necessity of alternative or parallel control systems (linear and hybrid topologies) so that the user can shift between structures according to task complexity.