An intelligent singular value diagnostic method for concrete dam deformation monitoring

Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.

Bibliometric analysis of research challenges and trends in urban underground space

The utilization and development of urban underground space play a crucial role in optimizing the layout of civic architecture and enhancing the urban ecological environment,which contributes toward increasing the overall carrying capacity and promoting sustainable development in megacities.To delve into the research progress of urban underground space,knowledge maps were created using the information visualization software VOSviewer.The literature was systematically extracted from three prominent databases,namely,Web of Science,Scopus,and China National Knowledge Infrastructure.According to the bibliometric analysis of the co-citation and core words co-occurrence,the trends and challenges of research on urban underground space were identified.As highlighted by the results obtained,it still remains highly challenging to achieve interdisciplinary collaboration in urban underground space research;the research trends of urban underground space consist of collaborative planning and whole life cycle sustainable development,multisource geological data informatization and resource evaluation,infrastructure design optimization,and intelligent construction.The knowledge map,drawn using bibliometric methods,offers a quantitative analysis of literature retrieval across various levels.It is recognized as an essential tool for exploring and identifying challenges and trends in urban underground space.

Construction risks of Huaying mount tunnel and countermeasures

The Chongqing-Guang＇an motorway is planned to cross Huaying mount at Jingguan town of Chongqing city. The whole mount is a colossal anticline whose core is consisted of coal measure strata （upper Permian Longtan formation P21） and the limbs are limestone strata （middle Triassic Leikoupo formation T21 and lower Triassic Jialingjiang formation T1j）. The tunneling is full of risks of collapse, gas explosion or gas outburst, water （mud） inrush, gas inrush because of existence of faults, high pressure gas, karst tectonics and coal goafs around the tunnel. In order to cope with the high risk, two main countermeasures were taken to ensure security of construction. One is geology prediction, and the other is automatic wireless real-time monitoring system, which contains monitoring of video, wind speed, poisonous gas （CH4, CO, H2S, SO2）, people location, and automatic power-off equipment while gas contents being more than warning threshold. These ascertained the engineering safety effectively.