This paper presents the results of field tests performed to investigate the compressive bearing capacity of pre-bored grouted planted (PGP) pile with enlarged grout base focusing on its base bearing capacity. The bi-directional O-cell load test was conducted to evaluate the behavior of full scale PGP piles. The test results show that the pile head displacements needed to fully mobilize the shaft resistance were 5.9% and 6.4% D (D is pile diameter), respectively, of two test piles, owing to the large elastic shortening of pile shaft. Furthermore, the results demonstrated that the PHC nodular pile base and grout body at the enlarged base could act as a unit in the loading process, and the enlarged grout base could effectively promote the base bearing capacity of PGP pile through increasing the base area. The normalized base resistances (unit base resistance/average cone base resistance) of two test piles were 0.17 and 0.19, respectively, when the base displacement reached 5% Db (Db is pile base diameter). The permeation of grout into the silty sand layer under pile base increased the elastic modulus of silty sand, which could help to decrease pile head displacement under working load.
The Xingmeng Orogenic Belt evolved through a long-lived orogeny involving multiple episodes of subduction and accretion. However, there is a debate on its tectonic evolution during the Late Paleozoic. Here, we report geochemical, geochronological, and isotopic data from strongly peraluminous granites and gabbro-diorites from the Sunidzuoqi–Xilinhot region. Zircon U–Pb ages suggest that the intrusive rocks were emplaced during the Early Carboniferous (333–322 Ma). The granites exhibit geochemical characteristics similar to S-type granites, with high SiO2 (72.34–76.53 wt.%), Al2O3 (12.45–14.65 wt.%), and A/CNK (1.07–1.16), but depleted Sr, Nb, and Ta contents. They exhibit positive εNd(t) and εHf(t) values (?0.3 to 2.8 and 2.7–5.7, respectively) and young Nd and Hf model ages (TDM2(Nd)=853–1110 Ma and TDM2(Hf)=975–1184 Ma), suggesting that they may be the partial melting products of heterogeneous sources with variable proportions of pelite, psammite, and metabasaltic rocks. The meta-gabbro-diorites from the Maihantaolegai pluton have low SiO2 (47.06–53.49 wt.%) and K2O (0.04–0.99 wt.%) contents, and demonstrate slight light rare earth element (REE) depletion in the chondrite-normalized REE diagrams. They have high zircon εHf(t) values (14.41–17.34) and young Hf model ages (TDM2(Hf)= 230–418 Ma), indicating a more depleted mantle source. The variations of the Sm/Yb and La/Sm ratios can thus be used to assess the melting degree of the mantle source from 5% to 20%, suggesting a quite shallow mantle melting zone. We propose that the petrogenesis and distribution of the strongly peraluminous granites and gabbro-diorites, as well as the tectonic architecture of the region, can be explained by a ridge subduction model. Based on these results, and previous studies, we suggest a southward ridge subduction model for the Sunidzuoqi–Xilinhot region. 相似文献
Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation. 相似文献
Acta Geotechnica - Grain morphology has significant impacts on the mechanical behaviors of granular materials. However, its influences on grain breakage are still poorly understood due to the... 相似文献