Damage detection of a thin plate using modal curvature via macrostrain measurement

Plate structures are employed as important structural components in many engineering applications. Hence, assessing the structural conditions of in-service plate structures is critical to monitoring global structural health. Modal curvature-based damage detection techniques have recently garnered considerable attention from the research community, and have become a promising vibration-based structural health monitoring solution. However, computing errors arise when calculating modal curvatures from lateral mode shapes, which result from unavoidable measurement errors in the mode shapes as identified from lateral vibration signals; this makes curvature-based algorithms that use a lateral measurement only theoretically feasible, but practically infeasible. Therefore, in this study, long-gauge fiber Bragg grating strain sensors are employed to obtain a modal curvature without a numerical differentiation procedure in order to circumvent the computing errors. Several damage indices based on modal curvatures that were developed to locate beam damage are employed. Both numerical and experimental studies are performed to validate the proposed approach. However, although previous studies have reported relative success with the application of these damage indices on a simple beam, only one damage index demonstrated the capability to locate damage when the stiffness of the local region changed near the sensor.     

Bayesian Chance‐Constrained Hydraulic Barrier Design under Geological Structure Uncertainty

The groundwater community has widely recognized geological structure uncertainty as a major source of model structure uncertainty. Previous studies in aquifer remediation design, however, rarely discuss the impact of geological structure uncertainty. This study combines chance‐constrained (CC) programming with Bayesian model averaging (BMA) as a BMA‐CC framework to assess the impact of geological structure uncertainty in remediation design. To pursue this goal, the BMA‐CC method is compared with traditional CC programming that only considers model parameter uncertainty. The BMA‐CC method is employed to design a hydraulic barrier to protect public supply wells of the Government St. pump station from salt water intrusion in the “1500‐foot” sand and the “1700‐foot” sand of the Baton Rouge area, southeastern Louisiana. To address geological structure uncertainty, three groundwater models based on three different hydrostratigraphic architectures are developed. The results show that using traditional CC programming overestimates design reliability. The results also show that at least five additional connector wells are needed to achieve more than 90% design reliability level. The total amount of injected water from the connector wells is higher than the total pumpage of the protected public supply wells. While reducing the injection rate can be achieved by reducing the reliability level, the study finds that the hydraulic barrier design to protect the Government St. pump station may not be economically attractive.     

Experimental investigation on the grain-scale compression behavior of loose wet granular material

The behavior of model granular materials (glass beads) wetted by a small quantity of liquid forming capillary bridges is studied by one-dimensional compression test combined with X-ray computed tomography (XRCT) observation. Special attention is paid to obtain very loose initial states (initial void ratio of about 2.30) stabilized by capillary cohesion. XRCT-based analyses involve spherical particle detection adapted to relatively low-resolution images, which enable heterogeneities to be visualized and microstructural information to be collected. This study on an ideal material provides an insight into the macroscopic compression behavior of wet granular materials based on the microstructural change, such as pore distance distribution, coordination number of contacts, coordination number of neighbors and number of contacts per grain.

     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.     

重力方法研究越南早新生代基底效果分析(英文)

为了提高用重力方法在越南研究早新生代基底结构特点中的效率,表明作者们提出了一个综合分析地质及地球物理资料的系统方法.获得的研究结果表明:1)一个大的平均密度差是在用重力方法研究越南的早新生代基底时效率的一个基本因素;2)用多维线性相关分析来有效研究和预测越南的早新生代基底岩石的埋藏深度与密度是有可能的;3)通过利用多维线性分析预测密度以及泊松系数使我们能预测早新生代基底岩石.     

A Novel Intelligent ELM-BBO Technique for Predicting Distance of Mine Blasting-Induced Flyrock

Natural Resources Research - Blasting is an economical technique for rock breaking in hard rock excavation. One of its complex undesired environmental effects is flyrock, which may result in human...     

Modeling soil desiccation cracking by analytical and numerical approaches

An energy approach is proposed as a complement to the stress approach commonly considered for investigating soil desiccation cracking. The elastic strain energies before and after crack initiation are estimated by both numerical and analytical solutions. The energy released by cracking is then compared with the fracture energy to discuss crack initiation conditions. This leads to combined energy and stress conditions for crack initiation following Leguillon's theory. An approximate analytical solution is derived from a variational formulation of the porous elastic body equations. A cohesive zone model and finite element code are used to simulate crack propagation in an unsaturated porous body. This analysis shows that the energy criterion is reached before the stress criterion, and this can explain unstable crack propagation at the beginning. The approximate analytical solution allows predicting correctly the crack depth and opening in its initiation stage.     

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Natural Hazards - Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high...