Research status, hot spots, difficulties and future development direction of microbial geoengineering

Microbial geoengineering technology,as a new eco-friendly rock and soil improvement and reinforcement technology,has a wide application prospect.However,this technology still has many deficiencies and is difficult to achieve efficient curing,which has become the bottleneck of large-scale field application.This paper reviews the research status,hot spots,difficulties and future development direction microbial induced calcium carbonate precipitation(MICP)technology.The principle of solidification and the physical and mechanical properties of improved rock and soil are systematically summarized.The solidification efficiency is mainly affected by the reactant itself and the external environment.At present,the MICP technology has been preliminarily applied in the fields of soil solidification,crack repair,anti-seepage treatment,pollution repair and microbial cement.However,the technology is currently mainly limited to the laboratory level due to the difficulty of homogeneous mineralization,uneconomical reactants,short microbial activity period and large environmental interference,incidental toxicity of metabolites and poor field application.Future directions include improving the uniformity of mineralization by improving grouting methods,improving urease persistence by improving urease activity,and improving the adaptability of bacteria to the environment by optimizing bacterial species.Finally,the authors point out the economic advantages of combining soybean peptone,soybean meal and cottonseed as carbon source with phosphogypsum as calcium source to induce CaCO3.

Seismic risk evaluation for a planning mountain tunnel using improved analytical hierarchy process based on extension theory

Seismic risk evaluation(SRE) in early stages(e.g., project planning and preliminary design)for a mountain tunnel located in seismic areas has the same importance as that in final stages(e.g.,performance-based design, structural analysis, and optimization). SRE for planning mountain tunnels bridges the gap between the planning on the macro level and the design/analysis on the micro level regarding the risk management of infrastructural systems. A transition from subjective or qualitative description to objective or quantitative quantification of seismic risk is aimed to improve the seismic behavior of the mountain tunnel and thus reduce the associated seismic risk. A new method of systematic SRE for the planning mountain tunnel was presented herein. The method employs extension theory(ET)and an ET-based improved analytical hierarchy process. Additionally, a new risk-classification criterion is proposed to classify and quantify the seismic risk for a planning mountain tunnel. This SRE method is applied to a mountain tunnel in southwest China, using the extension model based on matter element theory and dependent function operation.The reasonability and flexibility of the SRE method for application to the mountain tunnel are illustrated.According to different seismic risk levels and classification criteria, methods and measures for improving the seismic design are proposed, which can reduce the seismic risk and provide a frame of reference for elaborate seismic design.

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.