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Accurate prediction of short-term rockburst has a significant role in improving the safety of workers in mining and geotechnical projects. The rockburst occurrence is nonlinearly correlated with its influencing factors that guarantee imprecise predicting results by employing the traditional methods. In this study, three approaches including including t-distributed stochastic neighbor embedding (t-SNE), K-means clustering, and extreme gradient boosting (XGBoost) were employed to predict the short-term rockburst risk. A total of 93 rockburst patterns with six influential features from micro seismic monitoring events of the Jinping-II hydropower project in China were used to create the database. The original data were randomly split into training and testing sets with a 70/30 splitting ratio. The prediction practice was followed in three steps. Firstly, a state-of-the-art data reduction mechanism t-SNE was employed to reduce the exaggeration of the rockburst database. Secondly, an unsupervised machine learning, i.e., K-means clustering, was adopted to categorize the t-SNE dataset into various clusters. Thirdly, a supervised gradient boosting machine learning method i.e., XGBoost was utilized to predict various levels of short-term rockburst database. The classification accuracy of XGBoost was checked using several performance indices. The results of the proposed model serve as a great benchmark for future short-term rockburst levels prediction with high accuracy.

Penelitian ini bertujuan untuk mengetahui dan memahami faktor-faktor apakah yang mempengaruhi efektivitas pembagian harta bersama pasca perceraian studi kasus perkawinan poligami di Pengadilan Agama kelas 1A Makassar. Penelitian ini menggunakan tipe penelitian hukum empiris atau (Socio Legal Study Research). Penelitian ini dilakukan di Kota Makassar. Hasil penelitian menunjukan bahwa efektivitas pembagian harta bersama pasca perceraian studi kasus perkawinan poligami dapat dilihat berdasarkan lima faktor, antara lain: faktor hukumnya; faktor penegakan hukum; faktor sarana atau fasilitas pendukung; faktor masyarakat; serta faktor kebudayaan. Perolehan data dari keseluruhan faktor menunjukkan hasil yang kurang efektif, antara lain: terdapat 65% terkait faktor hukumnya; 72,5% terkait faktor penegakan hukumnya; 60% terkait faktor sarana atau fasilitas pendukung; 61,25% terkait faktor masyarakat; serta 62,5% terkait faktor kebudayaan.

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Abstract. In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as primitives. For the last decade, significant advancement in object detection and representation using visual data has been achieved, but the primitive-based object representation still remains as a challenging vision task. Thus, a high-quality spatial map is mainly produced through complex labour-intensive processes. In this paper, we propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes. Extensive experiments show that our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously. In terms of building extraction accuracy, computation efficiency and boundary regularization performance, our model outperforms the state-of-the-art baseline models.

Increase of energy absorption along with smooth load displacement curve, reduced peak force and high mean crushing force is the key in the modern dynamic design of structures. In this regard, a new metallic tubular configuration consisting of uni-sectional bi-tubular inner tubes, with outer tubes of multiple varied cross-sections is proposed and crushed under axial dynamic loading. A number of configurations are proposed ranging from simple to complex polygonal sections defined in three groups. Deformation modes and energy absorption characteristics such as peak crushing force, mean crushing force, and specific energy absorption are determined and discussed for each configuration. The proposed arrangement shows a stable crushing and higher values of crush force efficiency. In order to select the most suitable configuration, on the basis of maximum specific energy absorption, peak crushing force and minimum peak force, a robust decision making method known as Complex Proportional Assessment (COPRAS) is implemented. The optimal configuration in each group is determined on the basis of higher values of specific energy absorption, crush force efficiency and a lower value of peak crushing force, using the chosen weighting factors in COPRAS implementation. Finally, the configuration with inner and outer hexagonal tubes is found to be the best possible design concept among the top members of each group, with peak crushing force, mean crushing force and crush force efficiency values of 69.8 KN, 7.3 KJ and 0.75, respectively.

The uniaxial compressive strength (UCS) of rock is one of the essential data in engineering planning and design. Correctly testing UCS of rock to ensure its accuracy and authenticity is a prerequisite for assuring the design of any rock engineering project. UCS of rock has a broad range of applications in mining, geotechnical, petroleum, geomechanics, and other fields of engineering. The application of the gradient boosting machine learning algorithms has been rarely used, especially for UCS prediction, and has performed well, based on the relevant literature of the study. In this study, four gradient boosting machine learning algorithms, namely, gradient boosted regression (GBR), Catboost, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were developed to predict the UCS in MPa of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan, using four input variables such as wet density (ρw) in g/cm3; moisture in %; dry density (ρd) in g/cm3; and Brazilian tensile strength (BTS) in MPa. Then, 106-point dataset was allocated identically for each algorithm into 70% for the training phase and 30% for the testing phase. According to the results, the XGBoost algorithm outperformed the GBR, Catboost, and LightGBM with coefficient of correlation (R2) = 0.99, mean absolute error (MAE) = 0.00062, mean square error (MSE) = 0.0000006, and root mean square error (RMSE) = 0.00079 in the training phase and R2 = 0.99, MAE = 0.00054, MSE = 0.0000005, and RMSE = 0.00069 in the testing phase. The sensitivity analysis showed that BTS and ρw are positively correlated, and the moisture and ρd are negatively correlated with the UCS. Therefore, in this study, the XGBoost algorithm was shown to be the most accurate algorithm among all the investigated four algorithms for UCS prediction of soft sedimentary rocks of the Block-IX at Thar Coalfield, Pakistan.