Nannan Zong

Hybrid Genetic Algorithm for Elimination of Severe Stress Concentration in Railhead Ends

This paper presents a computational method for eliminating severe stress concentration at the unsupported railhead ends in rail joints through innovative shape optimization of the contact zone, which is complex due to near field nonlinear contact. With a view to minimizing the computational efforts, hybrid genetic algorithm method coupled with parametric finite element has been developed and compared with the traditional genetic algorithm (GA). The shape of railhead top surface where the wheel contacts nonlinearly was optimized using the hybridized GA method. Comparative study of the optimal result and the search efficiency between the traditional and hybrid GA methods has shown that the hybridized GA provides the optimal shape in fewer computational cycles without losing accuracy. The method will be beneficial to solving complex engineering problems involving contact nonlinearity.

An Alternate Learning Approach for Destructive Testing in Civil Engineering

An alternative learning approach for destructive testing of structural specimens in civil engineering is explored by using a remote laboratory experimentation method. The remote laboratory approach focuses on overcoming the constraints in the hands-on experimentation without compromising the understanding of the students on the concepts and mechanics of reinforced concrete structures. The goal of this study is to evaluate whether or not the remote laboratory experimentation approach can become a standard in civil engineering teaching. The teaching activity using remote-laboratory experimentation is presented here and the outcomes of this activity are outlined. The experience and feedback gathered from this study are used to improve the remote-laboratory experimentation approach in future years to other aspects of civil engineering where destructive testing is essential.