找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Applications of Artificial Intelligence in Tunnelling and Underground Space Technology; Danial Jahed Armaghani,Aydin Azizi Book 2021 The A

[復(fù)制鏈接]
樓主: 五個
11#
發(fā)表于 2025-3-23 10:53:41 | 只看該作者
Empirische Polizeiforschung IIIe projects, estimation of the TBM performance is?considered as a significant issue since it?can be an influential parameter related to the project?cost. Hence, many scholars tried to develop simple, applicable, and powerful methodologies for the prediction of TBM performance. The total developed met
12#
發(fā)表于 2025-3-23 14:35:09 | 只看該作者
13#
發(fā)表于 2025-3-23 19:57:54 | 只看該作者
Das Modell der Preisabsatzfunktiondo this, after reviewing the available literature, the data collected from the tunnel site and doing laboratory investigations, five important parameters, i.e., rock mass rating, Brazilian tensile strength, weathering zone, cutter head thrust force, and revolution per minute, were set as model input
14#
發(fā)表于 2025-3-24 01:01:56 | 只看該作者
15#
發(fā)表于 2025-3-24 02:20:28 | 只看該作者
Book 2021ve been applied and introduced by the researchers in this field. In addition, a critical review of the available TBM performance predictive models will be discussed in details. Then, this book introduces several predictive models i.e., statistical and intelligent techniques which are applicable, pow
16#
發(fā)表于 2025-3-24 08:06:34 | 只看該作者
17#
發(fā)表于 2025-3-24 13:47:30 | 只看該作者
18#
發(fā)表于 2025-3-24 18:23:37 | 只看該作者
19#
發(fā)表于 2025-3-24 19:02:45 | 只看該作者
2191-530X of available TBM performance predictive models in detail.Int.This book covers the tunnel boring machine (TBM) performance classifications, empirical models, statistical and intelligent-based techniques which have been applied and introduced by the researchers in this field. In addition, a critical r
20#
發(fā)表于 2025-3-25 02:07:58 | 只看該作者
Empirische Polizeiforschung IIIir accuracy level is only suitable (coefficient of determination ~0.6) in many cases. On the other hand, these techniques are not good if there are some outlier data samples in the database. The best model category for TBM performance prediction is related to machine learning (ML) and artificial int
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-19 12:17
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
神池县| 万宁市| 广汉市| 旌德县| 海丰县| 中超| 温泉县| 天水市| 阳东县| 左权县| 雷波县| 亳州市| 淳化县| 通化县| 仙桃市| 金平| 义马市| 古浪县| 枣阳市| 梁河县| 成安县| 江津市| 宣威市| 太原市| 阿荣旗| 抚顺县| 淮滨县| 永顺县| 新丰县| 简阳市| 建德市| 呼玛县| 青浦区| 九江县| 田东县| 汕尾市| 龙口市| 溆浦县| 肥东县| 白城市| 南安市|