GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

Applying dual-tree complex discrete wavelet transform and gamma modulating function for simulation of ground motions

The aim of this paper is to develop a stochastic-parametric model for the generation of synthetic ground motions (GMs) which are in accordance with a real GM. In the proposed model, the dual-tree complex discrete wavelet transform (DT-CDWT) is applied to real GMs to decompose them into several frequency bands. Then, the gamma modulating function (GMF) is used to simulate the wavelet coefficients of each level. Consequently, synthetic wavelet coefficients are generated using extracted model parameters and then synthetic GM is extracted by applying the inverse DT-CDWT to synthetic wavelet coefficients. This model simulates the time–frequency distribution of both wide-frequency and narrow-frequency bandwidth GMs. Besides being less time consuming, it simulates several dominant frequency peaks at any moment in the time duration of GM, because each frequency band is separately simulated by the gamma function. Moreover, the inelastic response spectra of synthetic GMs generated by the proposed model are a good estimate of target ones. Using the random sign generator in the proposed model, it is possible to generate any number of synthetic GMs in accordance with a recorded one. Because of these advantages, the proposed model is suitable for using in performance-based earthquake engineering.

     

混凝土内钢绞线的蚀坑形状及分布特征

为研究预应力钢筋的锈蚀特性,在自然气候条件下,采用内掺盐的方式,进行了混凝土内钢绞线的锈蚀实验．实验结果表明：钢绞线的蚀坑形状可抽象为2种典型形状,即椭球形和马鞍形;随着锈蚀率增大,蚀坑总个数增大,椭球形蚀坑出现的概率呈先增后减趋势;马鞍形蚀坑出现概率呈先减后增趋势,但变化幅度都不大;蚀坑的长度、宽度、深度都较好地呈对数正态分布．     

水轮发电机组上机架-定子系统动态特性参数敏感性分析

建立了上机架-定子耦联模型进行模态分析,考虑了定子铁芯和机座的热态工况。通过调整设计变量参数,进行灵敏度计算。结果表明,上机架立筋为最大影响因素,且系统频率并不随立筋厚度增大而持续增大,即存在极值点。     

基于加权最小二乘的结构模态参数与损伤识别

为提高利用最小二乘进行模态参数识别的精确度,提出了分频段加权最小二乘模态参数及损伤区域识别方法。分别基于各频率段能量和普通克里金插值作为权值,加权计算模态参数。将此方法应用于识别时变系统,用于Koyna混凝土重力坝在地震作用下损伤识别中。结果表明,识别的固有频率时程曲线可以准确地对结构损伤情况进行描述,根据坝体观测点在初始和损伤结束时刻的响应信号识别的归一化振型可以对结构损伤进行定位。该方法可以较大地提高结构模态参数的识别精度,并且准确地识别结构的损伤区域。     

任意裂纹面荷载作用下界面断裂分析

为研究裂纹面上作用的荷载对裂纹稳定性的影响,本文基于比例边界有限元方法提出裂纹面作用有任意方向、任意大小面荷载的界面应力强度因子求解模型.界面裂纹具有复数形式的应力奇异性指数,在任意裂纹面荷载作用下其奇异应力场更为复杂.应用本模型,径向的位移和应力可解析求解,无需网格细分即可自动反映裂尖的应力奇异性.裂纹面上的任意荷载首先可分解成平行于裂纹面以及垂直于裂纹面的分量,并进一步分解成有限项幂函数的和.对每个幂函数荷载解析求解,基于线性叠加原理获得结构在全部荷载作用下的解.该模型对各向同性材料和各向异性材料均适用.文中通过板承受裂缝面荷载时的应力强度因子求解的多个算例对该模型进行了验证和应用,对板的几何尺寸和双材料参数进行了敏感性分析,并应用于重力坝坝踵界面裂缝在水压力作用下的应力强度因子求解.     

基于Kanai-Tajimi模型的地震作用荷载新型识别方法

目的研究一种识别地震作用荷载的方法,为地震作用荷载的识别与相应结构的动力特性、损伤的识别提供一种新的途径,进一步为结构的安全性、损伤进程和耐久性等研究提供理论参考.方法利用Kanai-Tajimi模型将单位强度白噪声过程模拟为未知的地震作用,并将其作用在结构上,然后根据数值模拟计算得到的结构体系响应作为观测量,采用Kalman滤波对未知荷载状态加以识别.结果最终可以利用结构的加速度响应反演得出地震作用的等效荷载,在人工添加5%观测噪声的条件下,最大误差率为7%左右,而在人工添加10%观测噪声的条件下,最大误差率为8%左右.结论通过算例的验证,说明笔者所采用的方法精度较高,获得了较好的识别效果.     

环境温度影响下基于结构连续位移曲率组合的损伤识别方法

目的寻找对环境温度不敏感的损伤特征指标,消除环境因素在结构健康监测和损伤识别方法应用过程中的影响,准确识别损伤发生和定位损伤位置．方法对静力作用下的结构位移进行中心差分求取结构各节点的位移曲率,将任意相邻三个节点间静位移曲率进行线性组合构建新的损伤指标．利用北京一年的实测温度数据模拟环境温度变化,通过一个简支钢梁的仿真算例验证方法的有效性．结果算例中梁上各节点损伤指标不随温度的变化而变化,在损伤单元相邻两节点处出现正向跳跃,与损伤单元相邻的左右两单元的外侧节点处出现负向跳跃,而更远处的未损伤单元节点处的损伤指标则始终保持在0值附近波动,据此损伤单元得到准确定位．结论利用结构连续位移曲率组合的损伤识别方法可以有效消除环境因素的影响,准确识别损伤发生和定位损伤位置,无需结构健康状态基准数据,具有一定的噪声鲁棒性．     

基于“投资-效益”准则的RC桥墩全寿命总费用

基于性能的抗震设计于20世纪90年代兴起以来,目前在土木建筑方面的文献较多,但是关于桥梁的研究成果较少。采用五级钢筋混凝土桥墩结构性能水平,以钢筋混凝土桥墩墩顶位移角为量化指标,建立了钢筋混凝土桥墩性能水平与钢筋混凝土桥墩位移角限值之间一一对应的关系。将地震危险性分析与最优设防烈度结合起来,建立基于投资-效益准则的RC桥墩全寿命总造价计算的通用框架。     

On the energy-delay trade-off in geographic forwarding in always-on wireless sensor networks: A multi-objective optimization problem

The design and development of multi-hop wireless sensor networks are guided by the specific requirements of their corresponding sensing applications. These requirements can be associated with certain well-defined qualitative and/or quantitative performance metrics, which are application-dependent. The main function of this type of network is to monitor a field of interest using the sensing capability of the sensors, collect the corresponding sensed data, and forward it to a data gathering point, also known as sink. Thus, the longevity of wireless sensor networks requires that the load of data forwarding be balanced among all the sensor nodes so they deplete their battery power (or energy) slowly and uniformly. However, some sensing applications are time-critical in nature. Hence, they should satisfy strict delay constraints so the sink can receive the sensed data originated from the sensors within a specified time bound. Thus, to account for all of these various sensing applications, appropriate data forwarding protocols should be designed to achieve some or all of the following three major goals, namely minimum energy consumption, uniform battery power depletion, and minimum delay. To this end, it is necessary to jointly consider these three goals by formulating a multi-objective optimization problem and solving it. In this paper, we propose a data forwarding protocol that trades off these three goals via slicing the communication range of the sensors into concentric circular bands. In particular, we discuss an approach, called weighted scale-uniform-unit sum, which is used by the source sensors to solve this multi-objective optimization problem. Our proposed data forwarding protocol, called Trade-off Energy with Delay (TED), makes use of our solution to this multi-objective optimization problem in order to find a “best” trade-off of minimum energy consumption, uniform battery power depletion, and minimum delay. Then, we present and discuss several numerical results to show the effectiveness of TED. Moreover, we show how to relax several widely used assumptions in order to enhance the practicality of our TED protocol, and extend it to real-world network scenarios. Finally, we evaluate the performance of TED through extensive simulations. We find that TED is near optimal with respect to the energy × delay metric. This simulation study is an essential step to gain more insight into TED before implementing it using a sensor test-bed.