Wavelet‐Based Method for Generating Nonstationary Artificial Pulse‐Like Near‐Fault Ground Motions

A method to generate a suite of artificial near‐fault ground motion time histories for specified earthquakes is presented. A wavelet‐based nonstationary (WB‐NS) model has been employed to effectively capture the time‐varying frequency content of a particular acceleration record and continuous wavelet transform has been used to simulate the largest velocity pulse. Furthermore, an iterative procedure using discrete wavelet transform is utilized to modify an earthquake ground motion and generate energy‐compatible ground motion. Eventually, the artificial near‐fault accelerogram is achieved via the superposition of a coherent extracted velocity pulse with a random acceleration record corresponding to a WB‐NS model and multiplied by a time‐modulating envelope function. The effectiveness of the method is demonstrated by comparing the spectral response and Arias intensity curves of the simulated accelerograms with those of the real records.     

Context-driven mean residual life estimation of mining machinery

Maintenance is crucial to ensure production/output and customer satisfaction in the mining sector. The cost of maintenance of mechanised and automated mining systems is very high, necessitating efforts to enhance the effectiveness of maintenance systems and organisation. For effective maintenance planning, it is important to have a good understanding of the reliability and availability characteristics of the systems. Determining the Mean Residual Life (MRL) of systems allows organisations to more effectively plan maintenance tasks. In this paper, we use a statistical approach to estimate MRL and consider a Weibull proportional hazard model (PHM) with time-independent covariates to model the hazard function so that the operating environment could be integrated into the reliability analysis. The paper explains our methods for calculating the conditional reliability function and computing the MRL as a function of the current conditions. The model is verified and validated using data from the hydraulic system of LHD equipment in a Swedish mine. The results are useful to estimate the remaining useful life of such systems; the method can be used for maintenance planning, helping to control unplanned stoppages of highly mechanised and automated systems.     

Effectiveness of modified pushover analysis procedure for the estimation of seismic demands of buildings subjected to near‐fault earthquakes having forward directivity

Near‐fault ground motions with long‐period pulses have been identified as being critical in the design of structures. These motions, which have caused severe damage in recent disastrous earthquakes, are characterized by a short‐duration impulsive motion that transmits large amounts of energy into the structures at the beginning of the earthquake. In nearly all of the past near‐fault earthquakes, significant higher mode contributions have been evident, resulting in the migration of dynamic demands (i.e., drifts) from the lower to the upper stories. Due to this, the static nonlinear pushover analysis (PA) (which utilizes a load pattern proportional to the shape of the fundamental mode of vibration) may not produce accurate results when used in the analysis of structures subjected to near‐fault ground motions. The objective of this paper was to improve the accuracy of the pushover method in these situations by introducing a new load pattern into the common pushover procedure. Several PAs are performed for six existing reinforced concrete buildings that possess a variety of natural periods. Then, a comparison is made between the PA results (with four new load patterns) and those of FEMA‐356 with reference to nonlinear dynamic time‐history analyses. The comparison shows that, generally, the proposed pushover method yields better results than all FEMA‐356 PA procedures for all investigated response quantities, and is a closer match to the nonlinear time‐history responses. In general, the method is able to reproduce the essential response features providing a reasonable measure of the likely contribution of higher modes in all phases of the response. Copyright © 2009 John Wiley & Sons, Ltd.     

Operating Environment-Based Availability Importance Measures for Mining Equipment (Case Study: Sungun Copper Mine)

When a system’s performance is inadequate, the concept of availability importance can be used to improve it. The availability of an item depends on the combined aspects of its reliability and maintainability. In a system consisting of many subsystems, the availability of some subsystems is more important to system performance than others. The availability measure determines the priority of availability across subsystems. Most researchers only consider operation time and ignore the influence of the operating environment; therefore, their estimations are not accurate enough. In contrast to previous research, we focus on the influence of the operating environment on the system/subsystem’s characteristics with a view to prioritizing them based on the importance of availability. The paper considers part of the mining fleet system of Sungun copper mine, including the wagon drill, loader, bulldozer, and dump truck subsystems. We identify an ordered list of possibilities for availability improvement and suggest changes or remedial actions for each item to either reduce its failure rate or reduce the time required to repair it.     

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informativeness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because (i) it re-uses the same features extracted for detection, (ii) it aggregates features using integral images, and (iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that DeepProposals outperform most of the previous object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.     

Polyionene/Br3 Grafted on Magnetic Nanoparticles as an Efficient and Eco-Friendly Catalyst for the Metal Free Synthesis of 3,4-Dihydropyrimidin-2(1H)-Ones/Thiones

Silicon - A novel polyionene modified magnetic nanoparticle with a core-shell-shell structure was developed for utilization as a heterogeneous catalyst in the metal free synthesis of...     

Prediction of FRP-confined compressive strength of concrete using artificial neural networks

Strengthening and retrofitting of concrete columns by wrapping and bonding FRP sheets has become an efficient technique in recent years. Considerable investigations have been carried out in the field of FRP-confined concrete and there are many proposed models that predict the compressive strength which are developed empirically by either doing regression analysis using existing test data or by a development based on the theory of plasticity. In the present study, a new approach is developed to obtain the FRP-confined compressive strength of concrete using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as characteristics of concrete and FRP, the output node was FRP-confined compressive strength of concrete. The idealized neural network was employed to generate empirical charts and equations for use in design. The comparison of the new approach with existing empirical and experimental data shows good precision and accuracy of the developed ANN-based model in predicting the FRP-confined compressive strength of concrete.     

Successive collapse potential of eccentric braced frames in comparison with buckling-restrained braces in eccentric configurations

In the present research, the successive collapse potential of eccentric braced frames (EBF) in comparison with bucklingrestrained braces in eccentric configurations (BRBF-E) has been studied. Initially, five-story EBF and BRBF-E were analyzed and designed according to the guidelines of the American Institute of Steel Construction (AISC). Subsequently, the nonlinear dynamic analysis based on Unified Facilities Criteria (UFC) guidelines was used. The Alternate Path Method was used in the mentioned structures with various cases for removed columns. Both critical cases and successive collapse potential through values such as Impact Factor for columns, the end point vertical displacement of removed column and Demand-Capacity Ratio for link beams have been specified. The results indicate that removing edge columns caused further critical situation in EBF and BRBF-E compared to the other cases. The results elaborated that the BRBF-E has higher vulnerability than EBF. Furthermore, additional analysis were undertaken to establish the lateral stability of the removed columns. The results of the lateral stability analysis in one of the cases confirmed that the analyzed parameters were increased.     

Effects of in‐cycle strength degradation on collapse capacity of steel moment frames

In this study, the effects of in‐cycle strength degradation of steel moment connections are investigated on global behavior of multiple degree‐of‐freedom structures. Two types of degrading connection models are defined and compared with a bilinear non‐degrading model. Due to dispersion of the experimental test results on connections performance, two models are considered for each type. A probability assessment is carried out by implementation of incremental dynamic analysis to find the capacity of the structures for various performance levels. A sensitivity study is conducted on hysteresis parameters of connection models to investigate the effect of these parameters on global behavior of the structures. Due to increased dispersion of displacement demands observed in degrading cases, a reliability analysis is carried out to consider the effect of uncertainty on confidence level of the structures. Results show that in‐cycle strength degradation can lead to abrupt dynamic instability and as a consequence decrease in reliability of the system against collapse. Copyright © 2013 John Wiley & Sons, Ltd.