The endurance time(ET) method is a dynamic analysis in which structures are subjected to intensifying excitations, also known as ET excitation functions(ETEF). The ET method is a tool for structural response prediction. The main advantage of the ET method over conventional approaches is its much lower demand for computational efforts. The concept of acceleration spectra is used in generating existing ETEFs. It is expected that ETEF acceleration spectra increase consistently with time and remain proportional to a target spectrum. Nonlinear unconstrained optimization is commonly used to generate ETEFs. Generating new ETEFs is a complicated time-consuming mathematical problem. If the target acceleration spectrum changes, new ETEFs must be generated. This study intends to modify existing ETEFs to be compatible with a desired acceleration spectrum. This process, called spectral matching, obviates the need for using the complicated generating procedure in simulating new ETEFs. ETEFs spectral matching is introduced in this paper for the first time. A Fourier-based method for ETEFs spectral matching is proposed. This algorithm is then applied in a case study. Results are presented to prove the efficiency of the algorithm. 相似文献
Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.
Natural Resources Research - Natural resources are a nation’s wealth, and the use of this wealth depends on the nation’s developmental objective. The goal of this work is to determine... 相似文献
This study focuses on the shoreline change detection along the North Sinai coast in Egypt using geographic information system and digital shoreline analysis system (DSAS) during the elapsed period from 1989 to 2016. The measurement of shoreline variation is mainly described for three zones: zone I, El-Tinah plain bay; zone II, El-Bardawil Lake; zone III, El-Arish valley. The rates of shoreline changes in the form of erosion and accretion patterns are automatically quantified by four statistical parameters functioned in DSAS namely endpoint rate, net shoreline movement, linear regression rate (LRR), and least median of squares. LRR results elucidate that the western seaside of El-Tinah plain bay has experienced an extremely dynamic feature with an average erosion rate of ?8.17?m/year. The littoral drifts have been driven by eastward alongshore currents toward the east side of the bay to be accreted with an average rate of +4.28?m/year. Moreover, the shoreline has progressed west of El-Bardawil inlet (1), El-Bardawil inlet (2), and El-Arish harbor. Subsequently, the corresponding average beach growth rates are found to be +2.7, +8.5, and +6.5?m/year, respectively. In contrast, the shoreline on the down-drift side to the east has negatively retreated, and the corresponding beaches have regressed at rates of ?4.5, ?8.65, and ?2.9?m/year, respectively. 相似文献
In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. 相似文献