采动区地基、条形基础与框架结构共同作用机理研究

本文综合应用结构力学、矿山开采沉陷学、土力学、材料力学等相关理论,建立了采动区地基–条形基础–框架结构共同作用力学模型。该力学模型综合考虑了开采盆地形成过程中地表变形对建筑物的动态影响以及框架结构建筑物的基础和建筑物的长度等影响因素,从理论上分析了采动过程中上部结构变形、内力等变化规律及主要因素的影响规律,推导出计算采动区建筑物移动变形和各种附加内力的计算公式,通过计算实例进行了验证,为采动区上方框架结构建筑物的保护、加固和设计提供了理论依据和计算工具。     

采动区地面建筑物破坏原因分析及防护

本文分析了采动区不同地表变形对地面建筑物影响的破坏原因,总结了对新建、现役建筑物抗地表变形的方法,提出了采动区建筑物抗地表变形的几点建议,为采动区建筑物的研究和发展提供了科学的参考。     

采动区地面建筑物破坏原因分析及防护

本文分析了采动区不同地表变形对地面建筑物影响的破坏原因，总结了对新建、现役建筑物抗地表变形的方法，提出了采动区建筑物抗地表变形的几点建议，为采动区建筑物的研究和发展提供了科学的参考。     

基于厚硬岩层运动状态的采场应力转移模型及其应用

针对采场应力转移和厚硬岩层运动之间关系的问题,通过理论分析、数值模拟和微震监测等手段,首先,提出考虑水平应力影响的半封闭厚硬岩层采场空间模型及其平衡条件;其次,研究连续开采条件下覆岩状态及其载荷转移规律;最后,探讨采场竖直方向上水平应力转移机制。主要研究内容和结论:受水平应力影响的半封闭采场空间模型内,工作面开采同时伴随着覆岩破裂运动和应力转移2个过程,厚硬岩层不同运动状态是形成复杂覆岩结构和应力演化的主要原因;依据工作面两侧不同采动环境,划分工作面为首采、单侧沿空、两侧非充分采动、一侧非充分采动一侧充分采动和两侧充分采动等5种类型,分别得到了相应的工作面静态垂直应力大小;考虑水平应力集中影响的采场上覆厚硬岩层结构,得到了采场厚硬岩层水平应力集中估算方法及其拉伸破坏力学表达式。模型应用于5301工作面的开采实践,现场微震监测结果佐证了研究的合理性,并指导了工作面冲击危险性预测和安全开采,成果对相似条件矿井灾害防控具有指导意义。     

岩溶矿区采动裂隙发育及溶洞破坏特征相似模拟

针对岩溶矿区煤层开采采动裂隙发育规律及其对溶洞稳定的影响问题,通过构建岩溶洞区域煤层开采模型,对煤层开采条件下的岩溶洞破坏特征以及岩层移动特征进行了分析,分析结果表明:在强烈的采动影响下,岩溶洞顶板最容易发生破坏,导致采动裂隙发育高度在岩溶洞顶板方向达到最大;在非均匀采动影响下,岩溶洞两侧岩层呈现极不均匀下沉,岩层以岩溶洞为端点发生回转,致使岩溶洞顶板和底板发生破坏。若此时,岩溶洞处于浅埋状态,则其顶板裂隙与地表容易贯通,使得地表土体颗粒流失,而底板裂隙造成岩溶洞内充填物漏失,岩溶洞顶板裂隙和底板裂隙的共同作用为岩溶地表塌陷创造了有利的客观条件。因此,在岩溶地区进行煤层开采时,应防止岩溶洞区域受到非均匀采动的影响。     

深部开采地表沉陷规律研究分析

结合实例,通过对煤矿深部开采地表下沉与采动程度关系的分析,揭示了开采深部单一工作面下地表变形特征和深部开采较大采区的地表变形特征,最后得到了深部开采较大的地表变形规律。     

采矿引发地表变形对建筑物的损坏程度预测评估

以某煤矿为例,经实地野外调查,充分分析了矿区地形地貌、地层岩性、地质构造、工程地质特征等,预测评估了煤矿开采引发地表变形对建筑物的损坏程度,为矿方制定目标、提出决策等提供了科学的理论依据。     

特厚煤层综放采动底板变形破坏规律的综合实测

为了探究兖州煤田兴隆庄煤矿特厚煤层综放开采对底板岩层的变形破坏规律,应用钻孔应变感应法和超声成像技术对该矿某综放工作面进行了综合实测,获得了底板下不同深度应变增量随工作面推进的变化曲线和工作面推进过程中不同深度钻孔超声成像图片资料。通过5个应变传感器监测数据和大量钻孔超声成像图片的关联对比分析,基本确定了该工作面采动底板扰动深度和矿压作用下支撑压力的影响范围。研究结果表明：①该工作面底板采动扰动深度约为19 m,具有较明显分带性,即可分为采动扰动破坏带和采动扰动变形带,带厚分别约为16 m和3 m;②采动扰动破坏带属于整体塑性变形,其强度条件已基本丧失,但采动扰动变形带仍以弹性变形为主,具有良好的承载条件和较强的抗渗强度;③采动矿压超前和滞后显现明显,其对底板影响程度具有由浅及深而减小的特征。该综合实测方法的成功应用不但为综放开采巷道支护、老空水防治等提供重要信息,而且对深部即将开采的下组煤能否安全带压采掘研究也将具有重要参考价值。     

山区地形对采动建筑物的影响及保护煤柱留设研究

煤炭资源的大规模开采促进了国民经济的发展和人民生活的改善,但随之而来的开采沉陷问题对矿区建(构)筑物和生态环境造成了严重的破坏,影响到矿区人民的正常生活。因而开采沉陷一直是一个备受人们关注的问题。本文就针对山区地形对采动建筑物的影响进行了分析。同时也对保护煤柱留设进行了研究。     

地下与露天复合采动效应及边坡变形机理

由于在地下与露天复合开采影响下,两种采动影响域中的一部分相互重叠,致使其采动效应相互作用和相互叠加,从而组成一个复合动态系统。在理论与实例分析的基础上,研究地下与露天复合采动体系中边坡岩体的变形与破坏机理,并在此基础上推导出相关的评价方法。     

Dynamic wind response of tall buildings using artificial neural network

This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.     

Prediction of elastic modulus of normal and high strength concrete by artificial neural networks

In the present paper, application of artificial neural networks (ANNs) to predict elastic modulus of both normal and high strength concrete is investigated. The paper aims to show a possible applicability of ANN to predict the elastic modulus of both high and normal strength concrete. An ANN model is built, trained and tested using the available test data gathered from the literature. The ANN model is found to predict elastic modulus of concrete well within the ranges of the input parameters considered. The average value of the experimental elastic modulus to the predicted elastic modulus ratio is found to be 1.00. The elastic modulus results predicted by ANN are also compared to those obtained using empirical results of the buildings codes and various models. These comparisons show that ANNs have strong potential as a feasible tool for predicting elastic modulus of both normal and high strength within the range of input parameters considered.     

盐冻作用下混凝土力学性能研究及灰色预测

为探究混凝土的劣化规律,对混凝土的力学性能进行了预测,以试验室数据为基础,构建了各项力学性能指标的灰色GM(1,1)预测模型,预测各项指标的劣化规律.结果表明:以盐溶液作为冻融介质会加快试块的破坏,且不同盐溶液对试块的破坏程度存在一定差距;获得的灰色GM(1,1)预测模型精度满足要求,可用于预测灌区内混凝土建筑物的力学...     

基于神经网络的碳纤维布与混凝土极限粘结强度分析

碳纤维布与混凝土的极限粘结强度问题属于高度非线性问题,难以建立精确的数学表达式进行分析。对基于拉出试验的极限粘结强度数据进行分析,建立了人工神经网络,对极限粘结强度进行仿真预测。神经网络的建立考虑了碳纤维布的厚度、宽度、粘结长度、弹性模量、抗拉强度和混凝土试块抗压强度、抗拉强度、宽度这8个参数,运用了118组试验数据对网络进行训练,对15组数据进行了预测分析。将神经网络计算结果同4种经验公式计算结果进行比较,其精度明显高于其他4种模型。结果表明,运用人工神经网络对碳纤维布与混凝土的极限粘结强度进行预测是可行的。     

Exploring the possibility of assessing the damage degree of liquefaction based only on seismic records by artificial neural networks

This study presents a new approach to determine the damage degree of liquefaction caused by a large earthquake. We propose an artificial neural network (ANN) model based only on the seismic records of ground and define the degree of liquefaction “DDL” as a damage index. This ANN model predicts the degree of excess pore water pressure increase as the correct output label based on the seismic records obtained from the three-dimensional shaking table test. The proposed model achieved high accuracy, and the outcomes from training data indicated that the ANN model is suitable to function as a liquefaction assessment system. Further, to evaluate the applicability of the proposed ANN model in the real world, the datasets of waves from three actual seismic records were input to the ANN as validation data. The DDL judgment obtained was a good fit with the real phenomena observed.     

应用人工神经网络技术的建筑物成新度评估

针对建筑物成新度评估中存在的问题,利用人工神经网络理论,建立了建筑物成新度评估的人工神经网络模型,从而为其准确评估提供了科学的依据。     

Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

基于模态应变能与神经网络的钢网架损伤检测方法

神经网络通过对样本的学习,获得结构模态参数与损伤之间的映射关系。目前基于神经网络的损伤检测已经越来越广泛地使用在非破坏性损伤诊断当中。但对于大型结构而言,它的训练样本数量过大,将消耗大量的计算。所以如何降低神经网络的计算量使其可用于大型结构的损伤诊断是一个亟待解决的问题。为了解决这个问题,提出了空间钢网架损伤的两步诊断法:第一步,利用模态应变能对结构损伤的敏感性,判断出结构损伤的可能位置;第二步,利用神经网络从可能发生损伤的杆件中定位出实际损伤的位置,并进行损伤程度的判断。利用一个空间网架作为数值算例,进行可行性验证。结果表明此方法可以准确判断出结构的损伤位置以及损伤大小,是一种行之有效的方法。     

采矿诱发地震分类的探讨

为有利于矿震灾害的预测、预防和治理,针对目前矿震各种分类间尚未建立起相互联系体系的现状,应用该灾害机制的最新研究成果,按照有利于对症治理灾害的原则,照顾到已被普通接受的习惯,提出矿震层级分类的概念、原则和优越性。按照矿震发生受原生构造应力场作用方式、岩石介质物理力学性质及岩层力学结构对矿震的控制作用、矿震与采矿活动的相关性、次生应力场动力来源和矿震发生及破坏的部位分出5个层级16种矿震类型,强调区域构造应力场和开挖造成的岩体应力状态改变在矿震分类、研究和治理中作用的重要。     

Application of artificial neural network for the prediction of jaggery mass during drying inside the natural convection greenhouse dryer

In this paper, an attempt is made to predict the hourly mass of jaggery during the process of drying inside greenhouse dryer under the natural convection mode. Jaggery was dried until the constant variation in the mass of jaggery. Artificial neural network (ANN) is used to predict the mass of the dried jaggery on hourly basis. Solar radiation, ambient temperature and relative humidity are input parameters for the prediction of jaggery mass in each hour in the ANN modelling. The results of the ANN model are also validated with experimental drying data of jaggery mass. The statistical parameters such as root mean square error and correlation coefficient (R²) are used to measure the difference between values predicted by the ANN model and the values actually observed from the experimental study. It was found that the results of the ANN model and experimental are shown fairly good agreement.