基于改进云推理算法的塔架结构损伤识别

为了解决塔架结构的损伤识别问题,提出了基于应变能和改进云推理算法的损伤识别方法。首先描述了云模型的基本理论和数字特征,并给出了模态应变能的基本公式;然后分析了X条件云发生器和Y条件云发生器的基本算法和运行步骤,借助灰云模型建立相应的前件云和后件云规则,考虑了测量噪声的影响,利用云发生器生成多组云滴,并利用多模式下云滴的确定度和生成值构建了基本云推理算法及其损伤识别指标。基本云推理算法中常会产生不均匀发散的云滴,从而使计算结果产生一定的偏差,为了降低云滴发散产生的偏差影响,提出了基于损伤模式数量加权的云推理改进策略。计算结果表明:云推理算法可以较好地应用于塔架结构的损伤识别,其识别结果明显优于传统的应变能耗散率指标方法;而改进云推理算法进一步提高了识别的精度,优于基本云推理算法。     

基于L1正则化的随机梁式结构静力损伤识别方法

提出了一种结合摄动法和L1正则化方法的随机梁式结构静力损伤识别方法。考虑初始模型误差和测量误差的影响,建立了关于随机损伤指数的控制方程,并将摄动法和L1正则化方法相结合,对随机损伤指数的控制方程进行求解,进而从概率的角度对结构的损伤进行识别。损伤试验结果表明,和传统的最小二乘求解法相比,本文方法能够更为准确地识别多处局部损伤的位置及大小,对实际结构损伤检测具有较好的参考价值。     

振型归一化对梁结构柔度曲率损伤指标的影响

结构柔度矩阵需由质量矩阵归一化振型获得,而质量矩阵归一化振型难以直接测得,限制了柔度曲率类损伤指标的应用。为分析振型归一化方法对梁结构柔度曲率类损伤指标的影响,根据梁结构的刚度、弯矩和位移曲率的关系,建立了均布荷载作用下结构损伤前后位移曲率与损伤程度的理论表达式,实现定量分析均匀荷载面曲率结构损伤程度。提出P-范数振型归一化方法,通过均匀荷载面曲率指标推导了振型质量矩阵归一化系数差x_α与损伤程度的关系。以三跨连续梁算例对理论进行了验证,结果表明,损伤程度定量指标效果良好,不同P-范数振型归一化方法下,损伤程度的偏差可由2x_α估算;2-范数振型归一化方法的损伤识别结果与质量矩阵振型归一化结果最接近,故当无法获得质量矩阵归一化振型时,可采用2-范数归一化振型代替。     

Serum pharmacochemistry combined with multiple data processing approach to screen the bioactive components and their metabolites in Mutan Cortex by ultra‐performance liquid chromatography tandem mass spectrometry

Root cortex of Paeonia suffruticosa Andrews (Paeoniaceae), known as Moutan Cortex (MC), is known to have anti‐allergic and anti‐inflammatory properties. However, the constituents absorbed into blood after oral administration of MC remain unknown. A sensitive and rapid method by ultra‐high‐pressure liquid chromatography–electrospray ionization–quadrupole‐time‐of‐flight mass spectrometry (UPLC‐ESI‐Q‐TOF‐MS) technology and the MetaboLynx^TM software combined with multiple data processing approach (Mdpa) was established to investigate the absorbed constituents in rats after oral administration of MC, providing unique high‐throughput capabilities for drug metabolism study. A hyphenated electrospray ionization and quadrupole‐time‐of‐flight analyzer was used for the determination of accurate mass of the fragment ion in negative mode, with excellent MS mass accuracy and enhanced data acquisition. This rapid automated analysis method was successfully applied for screening and identification of the constituents absorbed and metabolized studies of MC after oral administration to rats. A total of 46 peaks were obtained from MC, 41 of which were tentatively characterized. In the VIP‐plot of orthogonal partial least‐squares discriminant analysis, 23 interesting ions in serum samples were extracted, and 16 parent components and seven metabolites were detected in vivo. The integrative serum pharmacochemistry technique, UPLC‐ESI‐Q‐TOF‐MS, and Mdpa method were successfully applied for rapid discovery of multiple components from MC. Copyright © 2013 John Wiley & Sons, Ltd.     

Parameter identification for cracks in elastic plate structures based on remote strain fields

A method for the detection of cracks in plate structures is presented. In contrast to most of the common monitoring concepts taking advantage of the reflection of elastic waves at crack faces, the presented approach is based on the strain measured at different locations on the surface of the structure. This allows both the identification of crack position parameters, such as length, location and angles with respect to a reference coordinate system and the calculation of stress intensity factors (SIF). The solution of the direct problem is performed on the basis of the BFM (body force method). The inverse problem is solved applying the particle swarm optimization (PSO) algorithm. The BFM is based on the principle of linear superposition which allows the calculation of the strain field in a cracked body. The strain at an arbitrary point in the structure is replaced by the strain provided by body force doublets in the uncracked structure. The doublets as well as external loads are parameters which have to be determined solving the inverse problem by minimizing a fitness function, which is defined by a square sum of residuals between measured strain distributions and computed ones for an assumed crack. The PSO algorithm applied to the fitness function operates on the basis of a swarm of candidate solutions. Once knowing loading and crack parameters, the SIF can be determined.     

Metabolite identification of tanshinol borneol ester in rats by high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry

Tanshinol borneol ester (DBZ) is a potential drug candidate composed of danshensu and borneol. It shows anti‐ischemic and anti‐atherosclerosis activity. However, little is known about its metabolism in vivo. This research aimed to elucidate the metabolic profile of DBZ through analyzing its metabolites using high‐performance liquid chromatography combined with electrospray ionization quadrupole time‐of‐flight mass spectrometry. Chromatographic separation was performed on an Agilent TC‐C₁₈ column (150 × 4.6 mm, 5.0 μm) with gradient elution using methanol and water containing 0.2% (v/v) formic acid as the mobile phase. Metabolite identification involved analyzing the retention behaviors, changes in molecular weights and MS/MS fragment patterns of DBZ and its metabolites. As a result, 20 potential metabolites were detected and tentatively identified in rat plasma, urine and feces after administration of DBZ. DBZ could be metabolized to O‐methylated DBZ, DBZ‐O‐glucuronide, O‐methylated DBZ‐O‐glucuronide, hydroxylated DBZ and danshensu. Danshensu, a hydrolysis product of DBZ, could further be transformed into 12 metabolites. The proposed method was confirmed to be a reliable and sensitive alternative for characterizing metabolic pathways of DBZ and providing valuable information on its druggability.     

Active wavelength selection for mixture identification with tunable mid-infrared detectors

This article presents a wavelength selection framework for mixture identification problems. In contrast with multivariate calibration, where the mixture constituents are known and the goal is to estimate their concentration, in mixture identification the goal is to determine which of a large number of chemicals is present. Due to the combinatorial nature of this problem, traditional wavelength selection algorithms are unsuitable because the optimal set of wavelengths is mixture dependent. To address this issue, our framework interleaves wavelength selection with the sensing process, such that each subsequent wavelength is determined on-the-fly based on previous measurements. To avoid early convergence, our approach starts with an exploratory criterion that samples the spectrum broadly, then switches to an exploitative criterion that selects increasingly more relevant wavelengths as the solution approaches the true constituents of the mixture. We compare this “active” wavelength selection algorithm against a state-of-the-art passive algorithm (successive projection algorithm), both experimentally using a tunable spectrometer and in simulation using a large spectral library of chemicals. Our results show that our active method can converge to the true solution more frequently and with fewer measurements than the passive algorithm. The active method also leads to more compact solutions with fewer false positives.     

A preliminary approach of dynamic identification of slender buildings by neuronal networks

The study of the dynamic behavior of slender masonry structures is usually related to the preservation of the historic heritage. This study, for bell towers and industrial masonry chimneys, is particularly relevant in areas with an important seismic hazard. The analysis of the dynamic behavior of masonry structures is particularly complex due to the multiple effects that can affect the variation of its main frequencies along the seasons of the year: temperature and humidity. Moreover, these dynamic properties also vary considerably in structures built in areas where land subsidence due to the variation of the phreatic level along the year is particularly evident: the stiffness of the soil–structure interaction also varies. This paper presents a study to evaluate the possibility of detecting the variation of groundwater level based on the readings obtained using accelerometers in different positions on the structure. To do this a general case study was considered: a 3D numerical model of a bellower. The variation of the phreatic level was evaluated between 0 and −20 m, and 81 cases studies were developed modifying the rigidity of the soil–structure interaction associated to a position of the phreatic level. To simulate the dispositions of accelerometers on a real construction, 16 points of the numerical model were selected along the structure to obtain modal displacements in two orthogonal directions. Through an adjustment by using neural networks, a good correlation has been observed between the predicted position of the water table and acceleration readings obtained from the numerical model. It is possible to conclude that with a discrete register of accelerations on the tower it is possible to predict the water table depth.     

Unsupervised cell identification on multidimensional X‐ray fluorescence datasets

A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X‐ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X‐ray fluorescence microscopy data. Possible future extensions are also discussed.