Data-driven methodologies hold the potential to revolutionize mechanized tunneling by informing decision-making. However, imbalanced data categories are prevalent within the realm of mechanized tunneling. Data-driven models often struggle to accurately predict data from minor sample categories, which, despite their scarcity, are crucial in practice. To address this bottleneck, this study introduces a committee-based active learning strategy to tackle the imbalanced sample identification problem. This strategy involves constructing multiple committee models to quantify the uncertainty in surrogate predictions. These specimens exhibiting high forecast uncertainty will be resampled with replacement from the validation data to iteratively augment the training data, thereby enhancing the model’s capability to handle imbalanced datasets. This study validates the strategy by applying it to two real tunneling engineering scenarios. The first case is to predict the grade of surrounding rock and the other one involves forecasting muck clogging in mechanised tunneling. The results demonstrate that the proposed strategy significantly improves the prediction accuracy of minority categories. Further, the study reveals that both the “entropy method” and “weight method” can effectively quantify the learning difficulty of samples. The same strategy can be equally applied to other fields of engineering and science.

The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of "operating by the flow" and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values.

The cornea is the main refractive medium of the human eye, and its clarity is critical to visual acuity. Corneal optical density (COD) is an important index to describe corneal transparency. Intact corneal epithelial and endothelial cells, regular arrangement of collagen fibers in the stroma, and normal substance metabolism are all integral for the cornea to maintain its transparency. In the last two decades, the Pentacam Scheimpflug imaging system has emerged as a breakthrough for the measurement of COD (also called corneal densitometry). It has been found that a wide variety of factors such as age, refractive status, and corneal diseases can affect COD. Different corneal refractive surgery methods also change COD in different corneal regions and layers and affect visual acuity following the surgery. Thus, COD has gradually become a significant indicator to evaluate corneal health, one on which the attention of clinicians has been increasingly focused.

Feeder-bus networks between urban rail transit systems and bus stops are inefficient in terms of the cost incurred by passengers, the time taken to reach the destination, and the number and frequency of feeder buses. To solve these issues, a new split delivery model using genetic algorithm (GA) for the feeder-bus network design problem (FBNDP), which is based on the transfer network, is proposed. To accomplish this, the general many-to-one assumption between bus stops and routes is discarded and replaced by the many-to-many assumption, wherein multiple bus stops are served by multiple routes, while considering a new split delivery method. This model is called the feeder-bus network design problem with split delivery (FBNDP-SD). The use of GA and the many-to-many assumption through repeat bus stops resulted in the effective distribution of passenger demand among different routes, reducing the passengers' generalized travel expenses and optimizing the network structure. While the construction of the FBNDP-SD solution, as well as the interconversion of the FBNDP solution and FBNDP-SD solution, is realized, this paper provides a new research direction of FBNDP.

: For urban rail transit, an environmentally-friendly transportation mode, reasonable passenger flow assignment is the basis of train planning and passenger control, which is conducive to the sustainability of finance, operation and production. With the continuous expansion of the scale of urban rail networks, passenger travel path decision-making tends to be complex, which puts forward higher requirements of networked transportation organization. Based on undirected graphs and the idea of the recursive divide-and-conquer algorithm, this paper proposes a hierarchical effective path search method made up of a three-layer path generation strategy, which consists of deep search line paths, key station paths composed of origin–destination (O-D) nodes and transfer stations, and the station sequence path between the key stations. It can effectively simplify the path search and eliminate obvious unreasonable paths. Comparing the existing research results based on the classical polynomial Logit model, a practical Improved C-Logit multi-path passenger flow assignment model is proposed to calculate the selection ratio of each path in the set of effective paths. Combining the hierarchical path search strategy, the O-D pairs of passenger flow are divided into local-line and cross-line situations. The time-varying cross-line passenger flow is decomposed into a series of passenger sections along the key station paths. A passenger flow pushing assignment algorithm based on line decomposition is designed, which satisfies the dynamic, time-varying and continuous characteristics. The validation of Guangzhou Metro’s actual line network and time-varying O-D passenger demand in 2019 shows that the spatio-temporal distribution results of the passenger pushing assignment have a high degree of coincidence with the actual statistical data.