Explore the vast range of titles available.

This paper presents a novel deep learning model for real-time prediction of shield moving trajectory during tunnelling. The proposed model incorporates a wavelet transform (WT) into Adam-optimised long short-term memory (LSTM) (WT-Adam-LSTM). The WT is employed to remove the irrelevant noise of data in the time and frequency domains, which allows the sequence pattern to be detected easily. The Adam algorithm is used to increase the reliability and optimise the gradient training process of the LSTM neural network for a given time series. The developed model considers the shield performance database, complex geological conditions, soil geometry, and operational parameters. A case study of a tunnel section under Bao'an International Airport was employed to verify the performance of the proposed model. A comparison with other models, i.e. recurrent neural network, LSTM, and support vector regression, was also made. The results show that WT-Adam-LSTM provides an effective solution and can achieve better results compared with other models.

This study presents an integrated approach to evaluate inundation risks, in which an algorithm is proposed to integrate the storm water management model (SWMM) into a geographical information system (GIS). The proposed algorithm simulates the flood inundation of overland flows and in metro stations for each designed scenario. It involves the following stages: (i) determination of the grid location and spreading coefficient and (ii) an iterative calculation of the spreading process. In addition, an equation is proposed to calculate the inundation around a metro station and to predict the potential inundation risks of the metro system. The proposed method is applied to simulate the inundation risk of the metro system in the urban centre of Shanghai under 50-year, 100-year, and 500-year rainfall intensities. Both inundation extent and depth are obtained and the proposed method is validated with records of historical floods. The results demonstrate that in the case of a 500-year rainfall intensity, the inundated area with a water depth excess of 300 mm covers up to 5.16 km2. In addition, four metro stations are inundated to a depth of over 300 mm.

The interaction between a waterproof curtain and withdrawal wells (or simply, the wall-well effect) is a key factor in controlling the environmental effects that occur during the dewatering of foundation pits. A series of laboratory investigations was conducted to study the changes in groundwater level during dewatering. The influence of the buried depth of the waterproof curtain placed in a dewatering confined aquifer (D), and the filter length of the pumping well (L), is investigated. Based on the laboratory test results, numerical simulations are used to analyse the ground settlement. The relationship between the approximate hydraulic gradients on both sides of the wall (Δi), or the ground settlement outside the excavation (S), using ratio RD (D divided by the thickness of the confined aquifer) or ratio RL (L divided by the thickness of the confined aquifer), can be simulated using a Boltzmann curve. The value of Δi increases while S decreases with an increase in RD, whereas Δi decreases while S increases with an increase in RL. The value of the contraflexure point of the Boltzmann curve is regarded as the effective value of RD or RL. Based on a comprehensive consideration, RD is suggested to be equal to or larger than the effective value, and RL is suggested to be equal to or lesser than the effective value.

The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.

When shield tunnelling is constructed in complex geological conditions using a tunnel boring machine, the disc cutter in the cutterhead easily wears to the failure state, particularly when the ground conditions are heterogeneous. This paper summarises the failure modes of the disc cutter in heterogeneous ground conditions into three categories, based on the observed wear data from field: (1) uniform disc cutter wear, (2) non-uniform disc cutter wear, and (3) breakage of cutter ring. Subsequently, the stress state of a disc cutter in the heterogeneous ground was analysed and the effective factors were investigated. The relationships between friction energy during cutting, working status of the machine and the characteristics of the geological conditions were evaluated. Based on the stress analysis and friction energy, a prediction model was proposed. The proposed model was applied to two field case studies: pertaining to uniform and mixed-face ground conditions, for which the empirical coefficient k for energy transfer was also determined. The preliminary results from this research indicated that the proposed model was valid for both homogeneous and heterogeneous ground conditions. Further case studies provided by co-operators are expected to improve the effectiveness of the proposed model.

Accurate estimation of the bearing capacity of piles requires complex modelling techniques which are not justified by timeframe, budget, or scope of the projects. In this study, six advanced machine learning algorithms including decision tree, k-nearest neighbour, multilayer perceptron artificial neural network, random forest, support vector regressor and extremely gradient boosting are employed to model the bearing capacity of piles in cohesionless soil, and the particle swarm optimisation algorithm is used to optimate the hyper-parameters of machine learning algorithms. A dataset comprising of 59 cases is employed and the R-squared value, root mean square error and variance accounted for are used as performance metrics to compare the performance of optimised machine learning methods. The comparison reveals that the optimised machine learning methods have great potential to estimate bearing capacity of piles and the particle swarm optimisation algorithm is efficient in the hyper-parameter tuning. The results show that R-squared values of six optimised machine learning approaches on the testing set vary from 0.731 to 0.9615. Also, the optimised extremely gradient boosting (R-squared value = 0.9615) shows the best performance compared with other algorithms. Furthermore, the relative importance of influential variable is investigated, which shows that effective stress is the most influential variable for bearing capacity of piles with an importance score of 30.9%. In addition, the results by the optimised machine learning method are compared to the β-method which is a popular empirical method. It is revealed the prominent performance of optimised machine learning approaches.

To promote sustainable development, the Chinese government launched a new municipal solid waste (MSW) classification strategy in 2017. Shanghai was selected as one of the first pilot cities for MSW classification. The Shanghai municipal government first established the new MSW classification policy in 2017. The Shanghai Municipal Solid Waste Management Regulation was published in 2019 and came into effect on 1 July 2019. This short communication reports on Shanghai’s new MSW classification policy and its implementation. The main content and measures adopted by Shanghai’s government to ensure the effective implementation of the new MSW classification policy are introduced. Besides, a SWOT (i.e., strengths, weaknesses, opportunities, and threats) analysis on the present policy and measures is conducted, and based on the results, some discussions and suggestions regarding the implementation of MSW classification in Shanghai and the whole of China are presented.

Waterproof curtains are used as barriers to block groundwater during deep excavations; however, in practice, barrier leakage occurs frequently. This paper establishes a three-dimensional (3D) fluid-solid-coupled finite element model to analyse the effect of barrier leakage below the excavation surface. Uniform and local barrier leakages in a confined aquifer are simulated for a constant flow rate and fixed drawdown. The simulated results show that barrier leakage causes a variation in the flow direction, drawdown of the groundwater head outside of excavation, and significant additional deformation in the longitudinal direction. The factors influencing drawdown and ground settlement induced by leakage include leakage location, leakage area, barrier penetration depth, pumping-well penetration depth, and aquifer anisotropy. In addition, the simulation results show that the drawdown-time curves under various leakage conditions have similar patterns.

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.

Soft structured clays usually exhibit complex behaviors, which can lead to difficulties in the determination of parameters and high testing costs. This paper aims to propose an efficient optimization method for identifying the parameters of advanced constitutive model for soft structured clays from only limited conventional triaxial tests. First, a new real-coded genetic algorithm (RCGA) is proposed by combining two new crossover and mutation operators for improving the performance of optimization. A newly developed elastic–viscoplastic model accounting for anisotropy, destructuration and creep features is enhanced with the cross-anisotropy of elasticity and is adopted for test simulations during optimization. Laboratory tests on soft Wenzhou marine clay are selected, with three of them being used as objectives for optimization and others for validation. The optimization process, using the new RCGA with a uniform sampling initialization method, is carried out to obtain the soil parameters. A classic genetic algorithm (NSGA-II)-based optimization is also conducted and compared to the RCGA for estimating the performance of the new RCGA. Finally, the optimal parameters are validated by comparing with other measurements and test simulations on the same clay. All comparisons demonstrate that a reliable solution can be obtained by the new RCGA optimization combined with the appropriate soil model, which is practically useful with a reduction in testing costs.