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The construction of super-large tunnels generates various safety risks that can hamper the tunneling process and cause severe damages if not properly identified. This paper presents a case study on the identification and management of geological and environmental risks during the construction of the largest (a diameter of 15.8 m) slurry shield tunnel in China. Its ground conditions and settlement control were identified among the most challenging owing to a large area of mixed ground conditions, 11 fault zones and tunneling under residential areas and the city’s Metro Lines 1 and 9. In response to the severity of these risks and challenges, novel monitoring systems, ground treatment, and safety management technologies were successfully implemented throughout the tunneling process. Further, a technical framework was proposed in this study to serve as a risk management guidance for analogous tunneling operations.

AbstractThis study proposed a rapid approach to determine the fuzzy number to evaluate roof fall risk in the coal mine. In risk assessments by fuzzy theory, the determination of fuzzy numbers using triangular fuzzy numbers (TFN) incorporated into the analytic hierarchy process is a difficult and time-consuming task. A novel approach to determine fuzzy number in TFN was proposed. To reduce occurrence of roof fall accidents, it is necessary to identify the main risk factors in roof fall risk assessments. A nine-score table-type questionnaire was adopted to collected expert judgement, based on which language scales were determined, ranging from 0.1 to 0.9, with increments of 0.1. Each language scale corresponded to the selected value of the experts. Subsequently, a triangular fuzzy complementary judgement matrix and a consistent possibility judgement matrix were constructed. The effectiveness of this proposed method was verified via analysing a case of roof collapse at Chaidar coal mine, Qinghai Province, China. The final ranking results of the risk factors were consistent with those observed in the field.

This paper presents a numerical investigation of the mechanical behaviour of segmental linings owing to the existence of voids in the composite lining. The composite lining was formed by mining outside and shield tunnelling inside with backfill in between. Finite element method was conducted to analyse the axial forces and bending moment for voids of different diameters at different positions. The results show that the axial forces in the transverse section increase near the crown and then invert and decrease on the left and right sides with an increase in the void centre angle (β) measured from the vertical axis. The increase in void diameter (d

This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross validation (K-CV). The SCA includes two learner layers: a primary learner's layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index (TPI) and field penetration index (FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method (EM) and silhouette coefficient (Si) are employed to determine the types of geological characteristics (K) in a K-means++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics.

An accurate prediction of earth pressure balance (EPB) shield moving performance is important to ensure the safety tunnel excavation. A hybrid model is developed based on the particle swarm optimization (PSO) and gated recurrent unit (GRU) neural network. PSO is utilized to assign the optimal hyperparameters of GRU neural network. There are mainly four steps: data collection and processing, hybrid model establishment, model performance evaluation and correlation analysis. The developed model provides an alternative to tackle with time-series data of tunnel project. Apart from that, a novel framework about model application is performed to provide guidelines in practice. A tunnel project is utilized to evaluate the performance of proposed hybrid model. Results indicate that geological and construction variables are significant to the model performance. Correlation analysis shows that construction variables (main thrust and foam liquid volume) display the highest correlation with the cutterhead torque (CHT). This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

A novel and effective artificial neural network (ANN) optimized using differential evolution (DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters. The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters (i.e. the epoch size, the number of neurons in a hidden layer, the number of hidden layers, and the regularization parameter) that govern the neural network efficacy. This approach is further enhanced by a stochastic gradient optimization algorithm to allow ‘expensive’ computation efforts. The ANN-DE is first trained using a prepared jet grouting dataset, then verified and compared with the prevalent machine learning tools, i.e. neural networks and support vector machine (SVM). The results show that, the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance. Specifically, the ANN-DE achieved root mean square error (RMSE) values of 0.90603 and 0.92813 for the training and testing phases, respectively. The corresponding values were 0.8905 and 0.9006 for the optimized ANN, then, 0.87569 and 0.89968 for the optimized SVM, respectively. The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches. A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability. In the hybrid stacking ensemble approach, we used an artificial bee colony (ABC) algorithm to find out the best combination of base classifiers (level 0) and determined a suitable meta-classifier (level 1) from a pool of 11 individual optimized machine learning (OML) algorithms. Finite element analysis (FEA) was conducted in order to form the synthetic database for the training stage (150 cases) of the proposed model while 107 real field slope cases were used for the testing stage. The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix, F1-score, and area under the curve, i.e. AUC-score. The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble (AUC = 90.4%), which is 7% higher than the best of the 11 individual OML methods (AUC = 82.9%). Then, a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction. The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method. Finally, the importance of the variables for slope stability was studied using linear vector quantization (LVQ) method.

This paper presents a case study on groundwater control and environmental protection during a deep excavation of the foundation pit for the Liyang Road Station of Metro Line 10 in Shanghai. A three-dimensional finite element simulation model was constructed to quantify the effects of a dewatering process on the environment around the excavation pit. To understand the decrease in the groundwater level around the foundation pit, the ground settlement and groundwater level were studied. During the excavation, environmental protection methods were used in the design of the optimal watering system to check its detrimental environmental effects. The effects of the diaphragm wall and horizontal barrier system were analyzed by varying their insertion depths and the resulting changes in the ground settlement and groundwater level were studied. It was found that increasing the insertion depth of the diaphragm wall reduced settlement near the excavation site but increased the construction costs. The diaphragm wall used in combination with a horizontal barrier was found to be effective in arresting the decrease in groundwater level and reducing settlement around the excavation site.

This article presents a case study on the risk assessment of geohazards along Cheng-Kun railway line. Cheng-Kun railway passes through a region of highly varied elevation and faults and the operation of Cheng-Kun railway has been frequently interrupted owing to geological disasters. A hybrid method via integrating triangular fuzzy number (TFN) and the analytic hierarchy process (AHP) (here after simply call TFN-AHP) into a geographic information system (GIS) is employed in this study to perform the geohazards assessment. The assessment results showed that TFN-AHP with GIS can effectively predict the distribution of geohazards risk in the study region within the last 10 years. The results also showed that the TFN-AHP method is more efficient in identifying high-risk areas compared with using original AHP. Based on the assessment results, prospective management measures to prevent risk are recommended.

This paper presents a review of the variation in the hydro-environment over the past four decades in Wuhan, China, and discusses its close relationship with flash flood events. These flash flood events have inundated underground infrastructures and resulted in significant economic losses and casualties, which are also discussed in relation to various external and internal factors. The external factors are predominantly owing to heavy rainstorms resulting from extreme climate change. The internal factors predominantly include a decrease in wetland areas, changes in topography, and disrepair of drainage systems in urban areas. Since the 1970s, lakes in Wuhan have been significantly reduced in size (by approximately 40%). This reduction in wetland area decreases the water storage capacity in urban areas, and the frequency of large flooding events thus increased from 0.1 event/year to approximately 0.2 event/year. Additionally, the disrepair of drainage systems in urban areas has reduced their ability to resist flood hazards. Changes in topography have also intensified the flood volume per unit time, further burdening the drainage systems. In 2016, Wuhan suffered from several large flooding events that led to some of the most severe economic losses in recorded history. The drainage system at the urban center of Wuhan and proposed flood prevention methods are discussed in detail in this paper. The sponge city (SPC) concept has been attempted in Qingshan District, Wuhan, and its success proved the reliability of the theory. A SPC is believed to reduce the impacts of future flash floods in China and other developed and developing countries. Keywords: Flash flooding, Wetland area, Drainage system, Wuhan, Sponge city

This paper proposes an intelligent framework to predict and automatically regulate earth pressure using a deep learning technique during earth pressure balance shield tunneling. A prediction model was proposed by integrating a new cost function (relative mean square error) with a gated recurrent unit (GRU). The moving average smoothing method was also incorporated into the GRU model to reduce the noise of the dataset and improve the accuracy of the proposed model. A real-time dynamic regulation model for adjusting the operational parameters was proposed by integrating the GRU model into a genetic algorithm-based optimizer. By adjusting the operational parameters, the dynamic regulation model regulates the excessive predicted earth pressure within a suggested range. The proposed prediction and regulation models were applied to a metro tunnel construction in Luoyang, China. The results show that the proposed models provide good guidance for automated tunnel construction.

This paper proposes a new computational model to predict the earth pressure balance (EPB) shield performance during tunnelling. The proposed model integrates an improved particle swarm optimization (PSO) with adaptive neurofuzzy inference system (ANFIS) based on the fuzzy C-mean (FCM) clustering method. In particular, the proposed model uses shield operational parameters as inputs and computes the advance rate as the output. Prior to modeling, critical operational parameters are identified through principle component analysis (PCA). The hybrid model is applied to the prediction of the shield performance in the tunnel section of Guangzhou Metro Line 9 in China. The prediction results indicate that the improved PSO-ANFIS model shows high accuracy in predicting the EPB shield performance in terms of the multiobjective fitness function [i.e. root mean square error (RMSE) = 0.07, coefficient of determination (R2) = 0.88, variance account (VA) = 0.84 for testing datasets, respectively]. The good agreement between the actual measurements and predicted values demonstrates that the proposed model is promising for predicting the EPB shield tunnel performance with good accuracy.

The data presented in this article pertain to field records of EPB shield machine in Metro Line No. 5 in Tianjin, China. Field performance of shield machine (cutterhead, screw machine, and shield advancing) are shown in the figures. Specifically, the database consists of the main parameters for shield tunnelling including cutterhead rotation speed, cutterhead torque, screw machine rotation speed, screw machine torque, shield thrust, and shield advance rate. In addition, the calculation process of energy consumption and variation index R2 during the tunnelling are displayed. The value of the dataset is the consideration of silt or clay soil encountered in the shield tunnelling area including the proportion of soils, grain gradation, and effects on performance and energy consumption of different parts in shield machine. These field data are applied to evaluate the construction efficiency in the article titled “Construction efficiency of shield tunnelling through soft deposit in Tianjin” [1].

Water quality is one of the most essential factors to influence human daily life and environment health. Risk assessment of water quality has critical significance to sustainable development of human society and natural systems. Set pair analysis (SPA) methods are widely used in risk assessment, especially in water resources. The essence of SPA is to classify assessment samples consider the uncertainties exist in risk assessment system based on the viewpoints of unity, difference, and opposition. The existing SPA methods are classified into two types, including (i) original SPA and (ii) comprehensive SPA. Both the original and comprehensive SPA methods have the following limitations: (i) it is need to judge whether the assessment factor belongs to type I or type II; (ii) it is need to judge whether the assessment factor is positive or negative. This method article gives a detailed description of the application of the existing SPA method. The method article is a companion paper with the original article [1].• Description of SPA methods.• Application of SAP methods in risk assessment of water quality.• Calculate the weights of assessment factors.

Monte Carlo simulation (MCS) is applied in the engineering with great fuzziness and uncertainty. Technique for order preference by similarity to an ideal solution (TOPSIS) method is used to deal with multi-criteria decision-making issue. Membership function is used to determine the membership degree of evaluated index. This paper presents the method for lake eutrophication level evaluation. The developed approach merges MCS method, TOPSIS method and membership function. The evaluated results are consistent with real eutrophication level in Lake Erhai, China. Global sensitivity analysis (GSA) is conducted. Results show that potassium permanganate index (CODMn) displays the highest negative correlation with the evaluated results and Secchi disc (SD) performs the highest positive correlation under different errors in measured data. The novelty of this work are: (1) the application of TOPSIS considers Surface water environmental quality standards and measured data. Besides, the Monte Carlo simulation method is applied to generate a normal distributed dataset to overcome the errors caused by human and equipment in data collection. The approach is utilized in the article, titled “Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels” (Lin et al., 2020) [1]. • Developed approach merges TOPSIS and MCS method. • It can increase the reliability of evaluated result.

The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled “Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements” (Zhang et al., 2020).

This data in brief presents the monitoring data measured during shield tunnelling of Guangzhou–Shenzhen intercity railway project. The monitoring data includes shield operational parameters, geological conditions, and geometry at the site. The presented data were arbitrarily split into two subsets including the training and testing datasets. The field observations are compared to the forecasting values of the disc cutter life assessed using a hybrid metaheuristic algorithm proposed for “Prediction of disc cutter life during shield tunnelling with artificial intelligent via incorporation of genetic algorithm into GMDH-type neural network” [1]. The presented data can provide a guidance for cutter exchange in shield tunnelling.

This article provides comprehensive experimental data of two polymers for shield tunnel sealing gasket: i) water swelling polyurethane (WSP) and ii) a mixture (WSRP) of WSP and water swelling rubber (WSR). Water-swelling tests are conducted to investigate the microstructural changes and properties of both WSP and WSRP during water swelling. These data can be useful for the quantitative evaluation of water swelling performance of WSP and WSRP. The data presented herein was used for the article, titled “Experimental investigation of water swelling characteristics of polymer materials for tunnel sealing gasket” [1].

The performance of neural networks with quadratic cost function (MSE cost function) is analyzed in terms of the adjustment rate of weights and its performance in multi-magnitude data processing using a qualitative mathematical method based on mean squared error. However, neural networks using quadratic cost functions exhibit low-weight updating rates and variations in performances in multi-magnitude data processing. This paper investigates the performance of neural networks using a quadratic relative error cost function (REMSE cost function). Two-node-to-one-node models are built to investigate the performance of the REMSE and MSE cost functions in adjustment rate of weights and multi-magnitude data processing. A three-layer neural network is employed to compare the training and prediction performances of the REMSE cost function and MSE cost function. Three LSTM networks are used to evaluate the differences between REMSE, MSE, and Logcosh in actual applications by learning stress and strain of soil. The results indicate that the REMSE cost function can notably accelerate the adjustment rate of weights and improve the performance of the neural network in small magnitude data regression. The applications of the REMSE cost function are also discussed.

The data presented in this paper pertain to case records of liquefaction potential surveys in earthquake prone areas. Field performances of 219 sites obtained from various regions (U.S.A, Japan, Turkey, China, Canada, etc …) are put on display. Specifically, this database consists of 253 cone penetration test (CPT) field records, among which 72 cases that did not liquefied and 181 cases that liquefied. In total, 10 principal variables are tabulated including the earthquake magnitude, maximum ground surface acceleration, depth, water depth, total overburden stress, effective overburden stress, Cone Penetration Test (CPT) tip resistance, CPT friction ratio, fines content, shear stress ratio. These data were arbitrarily split into a testing set of 53 cases and a training set of 200 cases. These field observations are compared to prediction values of liquefaction potential assessed using the evolutionary neural network proposed for “Evaluation of soil liquefaction with AI technology incorporating a coupled ENN/t-SNE model” [1]. Keywords: Liquefaction, Database, CPT, Neural network

Tunnel pressure from the surrounding rocks plays a critical role for the safety of tunnel. The existing methods for calculate twin-tunnel pressure supposed that the tunnel is buried in a uniform soil layer. This work presents detailed equations of an analytical method to calculate the twin-tunnel pressure in layered strata, which can consider the effects from soil layers. The proposed method is applied to analyse the pressure of the metro twin-tunnels in Chongqing. To demonstrate the efficiency of the proposed analytical method, both the tunnel pressure in layered strata and single strata were calculated. The method article is a companion paper with the original article [1].• Analyses of the soil parameters;• Determine the failure pattern A/B;• Calculate the vertical and horizontal pressure.

Floods in the metro system have caused catastrophic damages in mega-cities, especially in subsiding environment. This data in brief gives a detailed description for the calculation of judgment matrixes during decision making process for the flood risk assessment of the metro system. The data source of the assessment factors is provided. The analytical hierarchy process (AHP) and interval fuzzy AHP (FAHP) are used to calibrate the weights of assessment factors. The fuzzy clustering analysis (FCA) method is used to modify the weights obtained from AHP and interval FAHP. The data presented herein was used for the article, titled “Flood risk assessment of metro systems in a subsiding environment using the interval FAHP–FCA approach” Lyu et al. (2019) [1]. Keywords: FAHP, FCA, Flood risk, Subsiding environment, Metro system

This short communication reports on the recent incident of a foundation pit collapse at a construction site on 8 June 2019 in Nanning City of Guangxi Province, China. There were no injuries or casualties reported for this incident. This report presents the incident background, management measures taken after the incident, and a brief discussion of the causes of the incident. Some mitigation measures are suggested to prevent similar incidents in the future based on the preliminary analysis.

This short communication reports on a large landslide with a movement of 2 million m3 of soil and rock that occurred on 23 July, 2019 in the village of Pingdi, located in the county of Shuicheng, Guizhou Province, China. This landslide resulted in 42 deaths and 9 missing people. This report describes the preliminary investigation, rescue effort, and possible cause. The total rainfall in the 6 days prior to the landslide was 189.1 mm, which may be held responsible as the major cause. Some recommendations are proposed to reduce human casualties and property losses.

The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.

The interface shear strength properties of geogrid reinforced recycled foamed glass (FG) were determined using a large-scale direct shear test (DST) apparatus. Triaxial geogrid was used as a geogrid reinforcement. The geogrid increases the confinement of FG particles during shear; consequently the geogrid reinforced FG exhibits smaller vertical displacement and dilatancy ratio than FG at the same normal stress. The failure envelope of geogrid reinforced FG, at peak and critical states, coincides and yields a unique linear line possibly attributed to the crushing of FG particles and the rearrangement of crushed FG after peak shear state. The interface shear strength coefficient α is approximately constant at 0.9. This value can be used as the interface parameter for designing a reinforced embankment and mechanically stabilized earth (MSE) wall when FG is used as a lightweight backfill and triaxial geogrid is used as an extensible earth reinforcement. This research will enable FG, recently assessed as suitable for lightweight backfills, to be used together with geogrids in a sustainable manner as a lightweight MSE wall. The geogrid carries tensile forces, while FG reduces bearing stresses imposed on the in situ soil. The use of geogrid reinforced FG is thus significant from engineering, economical, and environmental perspectives.