| 14 | 0 | 17 |
| 下载次数 | 被引频次 | 阅读次数 |
为确保岩溶区基坑施工安全,基于工程实际,先开展项目区岩溶发育特征分析,并通过基坑变形组合预测成果构建变形预警指标,实现其预警分级研究。分析结果表明,基坑区内基岩的溶洞率36.28%,线溶洞率7.32%,溶洞规模0.45~3.46 m,具强烈的岩溶发育特征;同时,在变形预测过程中,组合预测结果具有较优的预测精度,可充分验证本文预测思路是合理有效的,且经预警分析,坑顶水平位移、深层水平位移的预警等级为Ⅲ级,地表沉降的预警等级为Ⅱ~Ⅲ级,坑底竖向位移的预警等级为Ⅱ级,因此,按不利原则,基坑后续施工应按Ⅲ级预警。本文研究可为基坑安全施工提供理论指导,为类似工程提供一定的参考。
Abstract:To ensure the safety of foundation pit construction in karst areas, based on practical engineering conditions, a preliminary analysis of the karst development characteristics in the project area was conducted. A deformation warning index was then established using the combined deformation prediction results to achieve a graded warning study. The analysis results indicate that the karst cave ratio within the foundation pit area is 36.28%, the linear cave ratio is 7.32%, and the cave size ranges from 0.45 to 3.46 meters, demonstrating strong karst development characteristics. Additionally, during the deformation prediction process, the combined prediction results exhibited relatively high accuracy, fully validating the rationality and effectiveness of the predictive approach proposed in the paper. Through warning analysis, the warning levels for horizontal displacement at the pit top, deep horizontal displacement, and surface settlement were classified as Level III, while the warning level for vertical displacement at the pit bottom was Level II. Therefore, adhering to the adverse principle, subsequent pit construction should proceed under Level III warning conditions. This study provides theoretical guidance for safe foundation pit construction and offers valuable references for similar projects.
[1] 高虎军,王涛. 基于数据动态模态分解的基坑沉降稳定性预警预测研究 [J]. 工程勘察, 2023, 51 (1): 35-41.
[2] 杨春阳,罗正高,祝建勋,等. 薄壁超宽地连墙基坑变形分析与设计优化 [J]. 工程勘察, 2025, 53 (6): 16-21,50.
[3] 黄达,朱双中,宋宜祥. 基于LSTM神经网络的基坑工程智能预警系统研发与应用 [J]. 工程地质学报, 2024, 32 (2): 667.
[4] 何烈民,崔春雨,王思瑞,等. 深大基坑自动化监测及智能预警平台 [J]. 科学技术与工程, 2023, 23 (31): 13542- 13549.
[5] 夏天,成诚,庞奇志. 基于长短时记忆网络的深基坑变形安全风险预警 [J]. 地球科学, 2023, 48 (10): 3925-3931.
[6] 李守雷,梁为群,陈晓斌,等. 城市地下空间安全监测与预警指标研究 [J]. 地质与勘探, 2024, 60 (1): 95-104.
[7] 彭亮,田浩,白刚刚,等. 基于优化组合预测模型的滑坡发展趋势评价 [J]. 广西大学学报(自然科学版), 2021, 46 (5): 1228-1235.
[8] 王飞. 基坑变形组合预测分析及安全性评价 [J]. 隧道建设(中英文), 2019, 39 (2): 204-210.
[9] 袁志明,李沛鸿,刘小生. 顾及邻近点的改进PSO-SVM模型在基坑沉降预测的应用研究 [J]. 大地测量与地球动力学, 2021, 41 (3): 313-318.
[10] 刘锦,李峰辉,刘秀秀. 优化GA-BP神经网络模型及基坑变形预测 [J]. 隧道建设(中英文), 2021, 41 (10): 1733-1739.
[11] 王景春,宋培林,王炳华,等. 基于EMD-PSO-ELM的基坑变形时变序列预测研究 [J]. 铁道标准设计, 2020, 64 (9): 103-108.
[12] 李秋全. 改进组合预测模型在铁路隧道变形预测中的应用 [J]. 长江科学院院报, 2018, 35 (11): 63-68.
基本信息:
中图分类号:TU753
引用信息:
[1]方筠,郭建国,庞旭卿,等.基于多类变形信息融合预测的基坑预警研究[J].工程勘察().
基金信息:
交通部西部交通科技项目(200131800020); 陕西省教育厅自然专项(16JK1171); 陕铁院科研基金项目(2023KYYB-12)
2026-05-18
2026-05-18
2026-05-18