Machine learning revealing overlooked conjunction of working volume and mixing intensity in anammox optimization.

Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter.

Probabilistic Seismic Response Prediction of Three-Dimensional Structures Based on Bayesian Convolutional Neural Network.

Seismic response prediction is a challenging problem and is significant in every stage during a structure's life cycle. Deep neural network has proven to be an efficient tool in the response prediction of structures. However, a conventional neural network with deterministic parameters is unable to predict the random dynamic response of structures.

Structural health monitoring of a reinforced concrete building during the severe typhoon Vicente in 2012.

The goal of this study is to investigate the structural performance of reinforced concrete building under the influence of severe typhoon. For this purpose, full-scale monitoring of a 22-story reinforced concrete building was conducted during the entire passage process of a severe typhoon "Vicente." Vicente was the eighth tropical storm developed in the Western North Pacific Ocean and the South China Sea in 2012.