巖深神經 的英文怎麼說
中文拼音 [yánshēnshénjīng]
巖深神經
英文
deep petrosal nerve-
This thesis primarily makes below the work of a few aspects : ( 1 ) the method to append the section line in the geological section plane and the geological plane graph intersected each other ; ( 2 ) the principle and method that automatic determinate of orebody on transverse section with the neural network theory was presented, and draw the slippery boundary of ore ; ( 3 ) the method that drawing the surveying well and lane way engineering in the geological and survey graph ; ( 4 ) the method to automatic calculation and statistics output ore reserves and grade ; ( 5 ) the method that automatic creating the geology cross section of the hole ; this thesis realizes automation for managing geological graph based on the substantive research work
本論文主要作了以下幾個方面的工作： （ 1 ）提出了在相交平面圖和剖面圖中添加剖面剖切線的方法； （ 2 ）提出了應用人工神經網路理論確定剖面中礦體界線的理論和方法以及礦巖界線圓滑的原理與方法； （ 3 ）實現了礦山生產實測工程數據處理與地測圖件的有機結合； （ 4 ）實現了礦塊地質品位和儲量的自動計算和統計輸出； （ 5 ）提出了中深孔炮孔排面地質剖面圖自動剖切的原理和方法。本論文在大量研究工作基礎上，實現了地測圖件管理的計算機自動化。( 2 ) taking the geology analysis as main way and combining the pre - test, predicating the tunnel ' s main geology condition ( rock quality, surrounding rock classification, long - wide joint, fault, ground water, crustal stress ), and predicating the main potential disaster ( collapse, breaking water, rockburst, large deformation ) ( 3 ) employing the stress test result and inverse analysis of the stress field, finding out the crustal stress " s distributing rule along the tunnel axes section : the highest crustal stress is 24mpa near the deepest spot, and predicting that the k2 + 260 ~ k3 + 000 likely to be the high crustal stress segment for this tunnel
通過地質分析、類比分析、監控量測、數值模擬、神經網路等方法，對鷓鴣山隧道潛在的主要地質災害（塌方、涌水、巖爆和大變形）進行了預測預報（ 3 ）在現場地應力測試成果的基礎上，運用數值計算進行應力場反演，基本查明了沿隧道軸線剖面的地應力分佈規律：地應力量級在最大埋深附近達到最大值24mpa ，並預測k2 + 260 k3 + 000段將很可能成為鷓鴣山隧道的高地應力段。 （ 4 ）塌方往往與斷層破碎帶及千枚巖相聯系。The main factors have been determined to be the depth and width of hole, the strength of surrounding rock, the types of detonator, the decoupling coefficient, burden and unit consumption form what has been discussed, the author make use of those major factors as standard of affection and studied bp neural networks " work principle, structure and defectiveness. a model of modified bp neural networks has been used to built model in order to identify selection and optimize of blasting in rock anchor beam. triumphant parameter design of blasting were selected as a example, in order to perform network of parameter design
確定以炸藥類型、最小抵抗線、孔深、炮眼間距、不耦合系數、線裝藥量、巖石強度和巖體完整度為主要影響因素；把改進的bp演算法應用在爆破參數優化設計中，建立以炸藥類型、最小抵抗線、炮孔深度、炮眼間距、線裝藥量、不耦合系數、巖石強度和巖體完整度為主要影響指標，選擇45例國內外成功的爆破參數設計實例為樣本，利用bp神經網路進行爆破參數優化設計，並通過現場保護層和巖臺修面爆破試驗以及對其松動圈范圍進行聲波測試，其測試結果最優的爆破方案其松動圈最小，這與神經網路進行參數優化設計的爆破方案相吻合。4. dicussing soil pressure ( supposed as p ) and level displacement ( supposed as s ) in condition that consideration time performance, the relation is p = ae, when the defomation of wall has evidently effect on the soil pressure. 5. the deformation of wall ( supposed as sw ) leads to soil layer sedimentation ( supposed as pw ), the research find pw = 1. 22sw 6. the artifical neural networks has vast and applied foreground in the information construcion of deep foundation excluding the accidental factor
4 、探討了考慮時間效應的土壓力和位移關系曲線，認為在當墻體的變形對土壓力分佈產生明顯影響時，二者間的關系可用一指數函數來表達： p = ae ~ （ （ / s ） ） 5 、墻體側向變形，導致墻後土體產生沉降，在本次研究中，排除掉基坑降水施工因素的影響，由墻體變形導致的墻後土體沉降，二者具有p _ w = 1 . 22s _ w關系； 6 、將基於時間窗口滾動多步神經網路預測方法和應用於深基坑工程墻體變形的預測工作，具有較高的預測精度，該方法在巖土工程變形預測方法中具有廣泛的應用前景。分享友人