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关键词： 岩溶塌陷   综合物探   碳酸盐岩   地质灾害  

摘要： 在岩溶区进行的工程施工,常常伴随着地面塌陷和地质沉降等地质灾害,这一现象的根源在于,在岩溶发展过程中,伴随着各种大小不一的溶洞和土洞,这些溶洞和土洞在自然环境和人为活动的作用下,又会加速塌陷的产生,从而破坏基础结构的稳定。岩溶塌陷是重要地质灾害之一,严重影响着我国的国土利用与建设工程的安全。为更好地辨识和评价可能存在的风险,本研究对岩溶区域类型进行研究,开展典型岩溶地质的物探调查工作。本文从物探勘探的角度出发,探讨了物探勘探在岩溶地质的具体运用,为下一步的防灾减灾工作奠定理论基础。

关键词： 边坡   桥桩   承载特性   抗压极限承载力  

摘要： 为研究斜坡桥桩承载特性的影响因素,本文应用有限元软件MIDAS GTS建立斜坡桥桩模型,研究了坡角、桩径、桥桩埋置深度及桥桩位置对桥桩承载特性的影响。结果表明,当边坡稳定性系数较高时,坡角增加对桥桩承载特性的影响较小,当边坡处于欠稳定状态或不稳定状态时,会极大影响桥桩的极限承载力,桥桩埋置深度与桩径的变化相似,桩径和埋深增加会导致桥桩极限承载力增加,但是桩径增至一定程度时,对极限承载力提升有限,桥桩位置对其极限承载力的影响较大,桥桩抗压极限承载力随桥桩与坡脚间水平距离增加而逐渐降低。

关键词： Tall buildings  

摘要： The authors previously presented an elasto-plastic analytical method of concrete-filled steel tubular (CFT) columns in consideration of fluctuation of axial force. In this paper, we conducted a seismic response analysis and an incremental loading analysis of a 40-story plane frame in which CFT columns are used. We compared the results between the proposed analysis model and a conventional one. In the results, the proposed model could simulate the elasto-plastic behavior of CFT columns more precisely. We show effectiveness of this analytical method for structural design of high-rise buildings. © 2025 Architectural Institute of Japan. All rights reserved.

关键词： Simulated annealing  

摘要： The characterized source model (0.2–10 Hz) composed of strong motion generation areas (SMGAs) of the 2024 MJ7.6 Noto Peninsula earthquake was estimated using the empirical Green's function method introducing the simulated annealing method. All nine SMGAs were located deeper than 4 km from the surface. The SMGA with the largest short-period spectral level A was located on the northeastern fault with the opposite dip direction from the other faults. The A of total SMGAs was slightly larger than the previous scaling models and four Mw 7.4 crustal earthquakes outside Japan. © 2025 Architectural Institute of Japan. All rights reserved.

关键词： 滑坡位移预测   集合经验模态分解   样本熵   深度神经网络   时间卷积网络  

摘要： 在对滑坡监测数据粗差进行有效处理及充分顾及滑坡监测数据自身特性的基础上,提出一种结合时序分解与相似分量重组的深度学习滑坡位移组合预测模型.首先,利用孤立森林法对滑坡时序监测数据的显著粗差进行处理,再对其平稳性、自相关性、正态性进行综合分析,确定模型预测中输入特征序列的最佳长度;其次,利用集合经验模态分解(EEMD)方法,将非稳态滑坡监测数据分解为多个平稳时间序列,再结合样本熵与K-means算法将其划分为高频、中频、低频3类时间分量;最后,通过对比不同神经网络模型的预测精度,分别构建适合于3类时间分量的预测模型,再将预测结果相叠加,实现对滑坡位移的高精度预测.实验区典型滑坡体北斗/GNSS监测数据测试表明,本文组合预测模型对含有显著粗差的滑坡监测数据具有较好的适用性,相较于单一及现有组合模型可显著提高滑坡位移预测精度.