Ji-Gang Xu

Screwed connections are widely adopted in timber–concrete composite (TCC) structures. Owing to the diverse connection configurations and complex shear mechanisms, existing empirical models or theoretical formulas cannot accurately and efficiently predict the shear modulus of a screwed connection. Therefore, this study develops machine learning (ML) algorithms to accurately predict the slip modulus. A data set including 222 sets of testing results was established by collecting the values of the slip modulus and associated ten features. Four ML methods, including decision tree (DT), random forest (RF), adaptive boosting machine (AdaBoost), and gradient boosting regression tree (GBRT), are adopted to develop the ML algorithm. The Shapley Additive Explanation (SHAP) framework was employed to interpret the effects of related features on the slip modulus. GBRT demonstrated the best accuracy compared with the other three ML methods in terms of four popular quantitative metrics. Moreover, all ML methods showed an evident accuracy advantage compared to existing analytical methods. Through a SHAP analysis, it was found that concrete strength, screw inclination, timber density, and timber type have a large impact on the slip modulus of a screwed connection compared to other input features.

With the rapid development of urbanization, the scales of urban population and land use are tremendously expanding

Rapid and accurate post‐earthquake damage evaluation of regional reinforced concrete (RC) bridges is a key issue for assessing the seismic resilience of cities and communities. Especially, RC bridges are susceptible to the aggressive environment, which can induce time‐dependent aging effects such as corrosion, and thus, it should be considered in the assessment. This paper presents an approach for regional seismic performance assessment of RC bridges in a life‐cycle context based on machine‐learning techniques. The life‐cycle seismic demand and capacity of the bridges are, firstly, obtained by the elaborated numerical model, which includes the deterioration induced by the aging corrosion effect. Then, the tagging‐based damage state (green, yellow, or red) is easily obtained by comparing the pairs of demand and capacity through machine learning. Four hundred and eighty bridge models are generated to develop the machine‐learning models and the performance of the machine learning models is evaluated. Results show that the extreme gradient boosting (XGBoost) model has the best performance, which has an accuracy of 81% in predicting the damage states. The proposed approach is demonstrated with a single bridge example and bridges in a sample region. It is shown that the machine learning model can accurately predict the post‐earthquake damage states of the single bridge, and it can also rapidly assess the damage states of the bridges in the sample region. Approximately 30% bridges in the sample region will experience damage states shift after 100 years, which highlights the importance of considering the aging effects on the post‐earthquake damage assessment of bridges.

The spherical multi-robot system is mainly used for water quality monitoring in large-scale aquaculture. The main research content of this article is how to realize the multi-robot system effectiveness of data transmission. Or, when the detection range is too large, the multi-robot system adopts the centralized control mode, once the instability of the central robot network occurs. If the data can not be fed back to the user, how to ensure the control ability of the entire multi-robot system. Novel network nod monitoring system has the advantages of Ad Hoc network, self - repair and not limited by the distance of router. It has been widely used in various fields. At the same time, cloud platform communication technology has also been widely used in the field of the Internet of Things. Its advantages are multi-purpose function, long transmission distance, strong safety performance, etc. By adopting self - owned cloud platform structure and the novel network nod monitoring system. It improves the flexibility and reliability of the multi-robot system and ensures the efficiency of large-scale data acquisition. In this paper, a Mesh network architecture and a network system based on cloud platform for spherical multi-robot systems are proposed. The experimental results show that it is feasible to construct the whole node monitoring system based on Mesh. With this system, the multi-robot system can accomplish more complex tasks.