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The severity and harmfulness of a rockburst event are significantly correlated with the scale of rock mass ejection, especially when the rock mass are not supported. This paper presents a microseismicity-based method for the early estimation of rockburst occurrence and its potential scale, which is graded according to the volume of the rockburst pit (Rv). The establishment of the estimation method involves a rockburst database, a grading scheme of the rockburst scale, selection and clustering analysis of rockburst samples, training of an artificial neural network (ANN) model, and dynamic updating. Firstly, a rockburst database is established from cases that were collected from the tunnels at depths of 1900–2525 m in the Jinping II hydropower station, located in southwest China. A grading scheme regarding the rockburst scale is preliminarily proposed on the basis of statistical analysis. Next, seventy-four rockburst cases, collected in tunnels with microseismic (MS) monitoring from October 2010 to March 2011, are selected as typical rockburst samples by using cluster analysis, and the relationships between the microseismicity and rockburst scale are deeply revealed. Then, three MS parameters, namely, the cumulative number of events, the cumulative energy, and the cumulative apparent volume, are determined and used together as input indicators for the identification of the rockburst scale. The estimation model is trained and cross-validated by the ANN optimized through genetic algorithm (GA). Finally, the performance of this microseismicity-based method has been validated by thirty-one cases that occurred in the tunnels with a cumulative length of 1.85 km, excavated from April 2011 to November 2011. The result indicates that approximately 83.9% of the rockburst cases could be reliably estimated. This study provides a new and feasible method for rockburst scale estimation, which can be used separately or applied as a complementary approach to current prediction methods for risk assessment and management of rockbursts in drill-and-blast tunneling.

Rockbursts cause serious casualties and damage in deep, hard rock tunnels. Structural planes can play an important role in the rockburst development process. In this paper, effects of structural planes on the microseismicity in rockburst development processes are summarized and studied in deep tunnels of the Jinping-II Hydropower Station, China. The study is based on an analysis of 44 moderate and intense rockbursts which occurred during a period of more than a year of microseismic (MS) monitoring and relates to 11.6 km of tunneling. The results show that most of the cases are affected by structural planes regardless of the rockburst’s moderate or intense character. The structural planes in the areas where rockbursts occur are mostly stiff ones and grade III or IV ones. In rockbursts development processes affected by structural planes, the MS energy proportion of big MS events, i.e. whose logarithm of MS energy (measured in Joules) is larger than 3.83, is comparatively lower. Structural planes change the way microseismicity evolves. Right from the beginning in the rockburst development and just before the rockburst affected by structural planes, there were some big MS events occurring. And structural planes cause rockbursts to occur more likely. Threshold values of the microseismicity (number of MS events and MS energy) for rockburst occurrence are relatively lower. In addition, the microseismicity in rockburst development processes with different intensities and with/without structural planes shows discreteness and the microseismicity is greater in more intense rockbursts.

ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2021 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. Languages issues: Concurrency, Objects, Coordination, Mobility, Higher order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming techniques. Programming support: Program analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization. Implementation: Compilation, Virtual machines, Memory management, Parallel and Distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. Related Paradigms and Synergies: Inductive and coinductive logic programming, Constraint logic programming, Answer set programming, Interaction with SAT, SMT and CSP solvers, Theorem proving, Argumentation, Probabilistic programming, Machine learning. Applications: Databases, Big data, Data integration and federation, Software engineering, Natural language processing, Web and semantic web, Agents, Artificial intelligence, Computational life sciences, Cyber-security, Robotics, Education.

Tunnel collapse causes serious casualties and economic losses. One typical case analysis of a collapse in deep-buried tunnel based on microseismic monitoring is presented. The results show that the number of microseismic event keeps increasing and the distribution of microseismic events becomes concentrated in space domain gradually during collapse nucleation process. And average distance squared decreases gradually during the imminent period time just before the collapse. The failure evolution mechanism of the collapse is analyzed by moment tensor method. It is noted that the failure mechanism between this kind of collapse and immediate strain-structure rockburst is similar. However, the proportion of shear and mixed fracture for collapse is higher than immediate strain-structure rockburst. It due to the rich structure planes in collapse zone.

The Frame-Stewart algorithm for the 4-peg variant of the Tower of Hanoi, introduced in 1941, partitions disks into intermediate towers before moving the remaining disks to their destination. Algorithms that partition the disks have not been proven to be optimal, although they have been verified for up to 30 disks. This paper presents a dynamic programming approach to this algorithm, using tabling in B-Prolog. This study uses a variation of the problem, involving configurations of disks, in order to contrast the tabling approach with the approaches utilized by other solvers. A comparison of different partitioning locations for the Frame-Stewart algorithm indicates that, although certain partitions are optimal for the classic problem, they need to be modified for certain configurations, and that random configurations might require an entirely new algorithm., Comment: Appeared in CICLOPS 2012. 15 Pages, 2 Figures

In the title complex, [Cu(C(16)H(11)Br(2)NO(4))(C(3)H(7)NO)](2), there are two unique mol-ecules in the asymmetric unit. Each Cu(II) atom is coordinated by two O atoms and one N atom from the tridentate ligand L(2-) [LH(2) = (E)-2-(3,5-dibromo-2-hydroxy-benzyl-idene-amino)-2-(4-hydroxy-phenyl)acetic acid] and the O atom of a dimethyl-formamide mol-ecule to give a slightly distorted square-planar geometry. The two unique mol-ecules form a dimer through weak C-H⋯O hydrogen bonds. In the dimer, the Cu⋯Cu distance is 3.712 (1) Å. In the crystal structure, mol-ecules form a one-dimensional chain through C-H⋯O hydrogen bonds. These are further aggregated into a three-dimensional network by O-H⋯O and C-H⋯O hydrogen bonds.

In the title complex, [Cu(C(16)H(11)Cl(2)NO(4))(C(3)H(7)NO)] , the Cu(II) atom is coordinated by two O atoms and one N atom from the tridentate ligand L(2-) {LH(2) = (2S)-[2-(3,5-dichloro-2-hydroxy-benzyl-idene)-imino]-3-(4-hydroxy-phenyl)propionic acid} and one O atom from a dimethyl-formamide mol-ecule, resulting in a slightly distorted square-planar geometry. The structure forms a one-dimensional chain through weak coordination bonds [Cu⋯O 3.080 (1), Cu⋯Cl 3.269 (1) Å] and a three-dimensional network through O-H⋯O and C-H⋯O hydrogen bonds.

In the title complex, [Cu(C16H11Cl2NO4)(C3H7NO)] , the CuII atom is coordinated by two O atoms and one N atom from the tridentate ligand L2− {LH2 = (2S)-[2-(3,5-dichloro-2-hydroxy­benzyl­idene)­imino]-3-(4-hydroxy­phenyl)propionic acid} and one O atom from a dimethyl­formamide mol­ecule, resulting in a slightly distorted square-planar geometry. The structure forms a one-dimensional chain through weak coordination bonds [Cu⋯O 3.080 (1), Cu⋯Cl 3.269 (1) A] and a three-dimensional network through O—H⋯O and C—H⋯O hydrogen bonds.