Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Liu, Yang; Chen, Hongyu; Zhang, Limao; Wang, Xianjia

doi:10.3846/jcem.2021.14901

Cited by 41 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The growth of each tree does not need pruning. In the process of tree generation, the variable eigenvalues of each node are only generated from several randomly selected feature subsets [14]. The generation process of the DT is shown in Figure 1.…”

Section: Construction Of Rfmentioning

confidence: 99%

Assessment of Water Pollution Degree in Qingshui River Basin Based on Stochastic Forest Algorithm

2021

WPPCP

View full text Add to dashboard Cite

Water is the active basis for the movement and transformation of various biological, physical and chemical substances and the flow of energy. On the one hand, the quality of water environment is very sensitive to changes in the external world; on the other hand, water quality affects the health of organisms and ecosystems through ecological support and other functions. The space-time distribution of water quality was analyzed according to the composition of basic substances and established water quality standards, which provided scientific basis for the rational development and utilization of water resources planning and management. In this paper, the stochastic forest algorithm was used to evaluate the water pollution degree of Qingshui River basin. This paper first analyzed the causes of Qingshui River pollution, and introduced the establishment method of random forest (RF) classification model. After that, this paper established the evaluation criteria of water pollution degree, and constructed the evaluation model of water pollution degree of Qingshui River basin according to the RF algorithm. In the experiment part, the quantity of decision trees (DT) of the RF model was set to 400 through sample training, and the classification accuracy of the RF classification model and the artificial neural network (ANN) model was compared. The experimental results showed that the RF classification model has a high classification accuracy rate of 97.33%, which can be used to assess the degree of water pollution. At the end of this paper, the RF classification model was used to evaluate the water pollution degree of the Qingshui River basin. The results showed that for the three stations in the Qingshui River basin, the water quality classification results are Class I, Class III, and Class IV. The water pollution degree of the Qingshui River basin is relatively serious.

show abstract

Section: Construction Of Rfmentioning

confidence: 99%

Assessment of Water Pollution Degree in Qingshui River Basin Based on Stochastic Forest Algorithm

2021

WPPCP

View full text Add to dashboard Cite

show abstract

“…For instance, Sresakoolchai et al develop deep machine learning models using three-dimensional recursive neural network-based co-simulation models to predict the next year’s track geometry parameters [ 21 ]. Liu et al establish a random forest seepage risk prediction model, which exhibits higher accuracy compared to traditional machine learning methods such as support vector machines and artificial neural networks [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels

Yin

Lei

et al. 2023

Sensors

View full text Add to dashboard Cite

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule “dislocation & spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at “water leakage → spalling”, with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel.

show abstract

“…Aulia et al (2019) used an RF to investigate automatic production history matching in reservoir engineering and ranked the importance of the input parameters. Liu et al (2021b) used an RF model to rank the importance of relevant factors in engineering specialty selection. Ding et al (2021) used an RF to rank the importance of the characteristic variables of artificial terraces.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Zhang,

Cao,

Xia

et al. 2023

Journal of Civil Engineering and Management

Self Cite

View full text Add to dashboard Cite

Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.

show abstract

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Cited by 41 publications

References 51 publications

Assessment of Water Pollution Degree in Qingshui River Basin Based on Stochastic Forest Algorithm

Assessment of Water Pollution Degree in Qingshui River Basin Based on Stochastic Forest Algorithm

Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels

Intelligent Prediction of the Frost Resistance of High-Performance Concrete: A Machine Learning Method

Contact Info

Product

Resources

About