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In order to clarify the law of top coal caving with continuous group caving method in extra-thick coal seams, this study investigated the influences of the caving openings width on the morphological characteristics of caving body, the evolution law of the boundary of top coal and their spatial relationship based on the discrete element numerical simulation test and theoretical analysis.The results show that when the top coal thickness is 12 m, the increase of the ratio of coal caving height to caving width (δ) would cause the increase in the eccentricity of caving body.The eccentricity of caving body tends to have a steady value when the δ value increases to a certain value.In group coal caving, the increase of caving openings width would lead to higher single caving weight and caving efficiency with increasing influence range of the boundary of top coal increases and decreasing boundary slope.The length and height of tangent section between the initial boundary of top coal and the adjacent caving body increases continuously, which benefit top-coal recovery amount.The tangency between the top-coal caving body and the boundary of top coal of the previous adjacent caving group can be used as an effective method to evaluate continuous group caving.

Macroscopic fundamental diagram (MFD)–based perimeter traffic flow control has been attempted to develop coordinated ramp metering (CRM) strategies for multibottleneck freeway networks operating around capacity. However, the effectiveness of these strategies may be limited by the minimum admissible on-ramp flow (e.g., due to queue saturation or minimum green time), potentially resulting in local congestion. This paper addresses these challenges from two aspects. First, a coordinated ramp control approach was developed to handle situations where the desired flow of an on-ramp falls below the minimum admissible flow. Second, variable speed limits (VSLs) were integrated into the coordinated RM to enlarge the available control space. The proposed approach employs a three-layered control structure. The first layer determines the total on-ramp flow entering the freeway network, using an extended MFD-based feedback perimeter regulator. This regulator utilizes dynamic network accumulation set points to enhance the regulator's effectiveness in the presence of heterogeneity. The second layer distributes the total on-ramp flow among the on-ramps, which includes determining the on-ramp flows without considering flow constraints and coordinating successive on-ramps to address potential constraint violations. The third layer determines VSL rates based on the control information of RM in the second layer and the coordinated control for successive VSLs. The proposed approach was evaluated using SUMO, a microscopic simulator, on a realistic freeway network (stretch), and compared with feedback RM, feedback VSLs, and their integration. Simulation results highlight that the proposed integrated controller (1) more effectively addresses congestion in the freeway, owing to its enhanced robustness and network-wide control benefits, (2) enlarges the control space to alleviate freeway congestion, and (3) performs better in terms of the density heterogeneity of the freeway. [ABSTRACT FROM AUTHOR]

In the literature, macroscopic fundamental diagram (MFD)- or network fundamental diagram-based perimeter control approaches have been demonstrated as cost-effective means to improve traffic efficiency for urban traffic networks. This article proposes a new perimeter control approach, which extends previous approaches in two aspects. First, the proposed approach optimizes the allocation of perimeter metered flow considering the heterogeneity of network traffic flow distribution, which is inversely related to network production. Second, queue management is considered by the proposed approach so as to prevent queue spillback induced by perimeter control. The proposed control approach is well advanced for field application using only loop detector data. It adopts a hierarchical framework, where the upper level controller determines the total flow entering the protected network through perimeter links based on an MFD-based feedback regulator, while the lower level controller optimizes the distribution of the total metered flow among the perimeter links based on real-time network heterogeneity and perimeter queues. The proposed approach is tested and compared with state-of-the-art perimeter control approaches, using a real traffic network developed in the microscopic simulation software VISSIM. Results demonstrate that the proposed approach significantly reduces the overall network delay. Moreover, it outperforms other perimeter control approaches by maintaining low network heterogeneity within the protected network and preventing (or delaying) queue spillback at perimeter links. [ABSTRACT FROM AUTHOR]

The induced polarization (IP) phenomenon describes the variation of resistivity with frequency and this geophysical method has been widely used to classify rock properties. Various experimental models have been developed to describe the mechanism of IP effect. The model based on the generalized effective-medium theory of induced polarization (GEMTIP) is an extension of the classic Cole–Cole model by considering electromagnetic inductions. Compared to the Cole–Cole model, the GEMTIP model can effectively incorporate rock composition and morphology to provide a more precise complex resistivity response. However, adding one more grain type to the GEMTIP model will result in adding three more parameters to the inversion process. Even for the GEMTIP model with only three-phase spherical grains, seven parameters need to be inverted. In general, the inversion of GEMTIP model with more than three phases is characterized by high uncertainty. Traditional geophysical inversion methods such as global optimization and least-squares optimization have certain limitations in inverting IP parameters. The effectiveness of least-squares optimization algorithm depends on the setting of initial inversion parameters. Efficiently processing massive amounts of data is challenging for both least-squares optimization and global optimization algorithms. Considering the powerful search and generalization capabilities of neural network (NN), we develop a machine learning (ML) approach for efficient inversion of IP parameters based on long short-term memory (LSTM) networks with physical constraints. Compared with traditional methods, ML method can significantly reduce the computational cost and produce reliable inversion results. To validate the effectiveness of ML approach, we first conduct multiple synthetic model studies based on the Cole–Cole and the GEMTIP models, respectively. We then further demonstrate the effectiveness of NNs in recovering IP parameters from rock samples using measured data from man-made and field rock samples. The results show that the developed method can effectively obtain accurate inversion results based on Cole–Cole and GEMTIP models. [ABSTRACT FROM AUTHOR]