Based on the life cycle footprint analysis method, this paper undertakes a comprehensive assessment of tourism-related direct and indirect water consumption under four counterfactual tourism scenarios in China's Beijing–Tianjin–Hebei metropolitan region (Jing-Jin-Ji), which has been increasingly constrained by extreme water stress. Food consumption appears to have a major impact on the tourism water footprint (WF) such that high calorie-dominated diets are nearly five times more water intensive than vegetable diets for a typical foreign tourist. It is necessary to design specific policy to improve water-use efficiency in upstream agricultural production, in parallel with reduced high-calorie food intake in tourism products supply. Furthermore, transport mode is found to have significant impacts on domestic tourist WF due to the stark variation in water embodied in upstream fuel production and supply. Forecasts for tourism's WF under low and high scenarios suggest that tourism may account for a considerable proportion of total water use in Jing-Jin-Jiby 2020. Specifically, tourism patterns appear to be a determining factor influencing water consumption across different scenarios. It is argued that water policy needs to emphasise water-use efficiency to raise awareness of tourist WF by differentiating water prices for various purposes and segments of the tourism consumer market.
In the reciprocity-compensated type fiber-optic bi-directional strain-displacement sensor,a coupler splits the light into the two fiber bonded in the tension or compression sections,and the modulated lights are measured by two detectors.By introducing the reciprocity-compensated parameter between the tension-sensitized fiber and the compensation-sensitized fiber,the error derived by the power fluctuate of optical source can be eliminate efficiently in the fiber-optic strain-displacement sensor.In the micro-displacement rack,the displacement experiment indicates that the standard error was ～0.16 mm.
An analytical method to predict the fatigue crack growth of embedded flaw in metallic structure has been established by using a non-isotropic approach. Within Engineering Criticality Assessment, the embedded flaw is considered as planar elliptical defect located inside of structure wall thickness. In the analytical standard assessment procedure [1], since only the minor ligament (the shortest distance between material surface and embedded crack tip) is applied to calculate the stress intensity factor, the fatigue crack propagation prediction in height direction is symmetrical for each side. Therefore, the crack growth is overestimated when, as it usually is the case, the embedded flaw is not centered and/or submitted to non-uniform stress range in the wall thickness of the structure. The proposed method allows to predict the fatigue crack propagation rate in asymmetrical way, by taking into account respectively the minor and major ligament, in order to remove the above conservatism and consequently to improve the ECA results for embedded defects.
The spline finite strip method, which has a high precision, was adopted to calculate and predict the work roll temperature field and crown. This prediction model was used in the presetting system of shape and crown for 1 450 mm hot strip mill. It is found that the model can predict the thermal crown of a hot strip mill with high accuracy.
Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation.
ShanXi is one of the base of energy resources and heary chemical industry,especially coal resources are abundant.Since our country was built up before fifty years,it is coal industry that made decisive use for promoting ShanXi′s econorny,mean while made great contributions to our nationel econonry and social development.But the exploit is so excessive and inappropriate that the ecological environnent is rapidly degrading and geology disaster frequently happen.They are subsidence and breaking of the surface,and deteriorate of the farm land,as well as damage of the building ,and exhausting of the water resources,and the dying vegetation.All of which have not only destructed the environment of the local people′s life,but also restricted the economical continual development of ShanXi.The paper sums up the situation of ShanXi′s geology disaster which mining coal brought about.And put forward a proposal which we can prevent it.
A robust automated method for operational modal analysis (OMA) is in a great demand for processing a large amount of structural health monitoring data from engineering structures. This paper proposes an improved automated OMA approach based on data-driven Stochastic Subspace Identification (SSI) and clustering techniques with novel criteria. The framework of the proposed approach includes two main components, namely, “modal identification by SSI” and “automated interpretation of SSI output.” Three procedures including hard validation criteria removal, an improved statistics-based clustering procedure, and a developed cluster merging procedure are combined in the second component for automatically interpreting the stabilization diagram from the SSI output, without a priori knowledge on the modal parameters and no manual tuning during interpreting. Numerical validation results on a frame structure model demonstrate that the proposed approach is capable of identifying the vibration modes accurately, under a significant noise effect. No spurious modes are observed, and the physical modes can be accurately identified. Experimental studies on a steel frame structure in the laboratory and a real footbridge are conducted to demonstrate the robustness and applicability of using the proposed approach for automated OMA and modal tracking. Identification results are compared with baselines and those from an existing reference method to demonstrate the improvement and contribution made in the proposed approach on the automated OMA.
The main bridge of Zhongxian Changjiang River Bridge is a double-pylon and double-cable-plane cable-stayed bridge with span arrangement(205+460+205) m and the structural formation and stressing conditions of the stay cable anchor zone in pylon of the Bridge are complicated owing to the utilization of sharp radius(R=1.85 m) U-shaped prestressing tendons there.In this paper,the combined method of the full-scale sectional model test of the pylon and the spatial finite element analysis is used to investigate the actual working conditions of the anchor zone,assess the bearing capacity of the zone and eventually to study the stressing conditions and crack development laws of the zone.The results of the study have direct guidance to the construction of the Bridge.
According to the widespread problems of government-invested projects currently, this paper put forward the necessity of objective and overall performance evaluation. Given the example of cusp catastrophe, it analyzes the mechanism of multi-objective evaluation by applying catastrophe theory, establishes multi-hierarchy indicator system of engineering projects by adopting catastrophe series method. The paper also introduced this method to the example of inland waterway projects. The outputs are well in line with the facts. The indicator system is established based on the inner logical relations between catastrophe model internal mechanism and performance objectives. It avoids the subjective influence of weights design. The calculation is quite convenient and well applied in government-invested projects.
This paper proposes an improved Empirical Wavelet Transform (EWT) approach for structural operational modal identification based on measured dynamic responses of structures under ambient vibrations. Two steps are involved in the improved EWT approach. In the first step, the standardized autoregressive power spectrum of the measured response is calculated to define the boundaries of frequency components for the subsequent EWT analysis. The second step is to decompose the measured response into a number of Intrinsic Mode Functions (IMFs) by using EWT. When the Intrinsic Mode Functions are obtained, structural modal information such as natural frequencies, mode shapes, and damping ratios can be identified by using Hilbert transform and Random Decrement Technique. In numerical studies, a simulated signal is used to investigate the effectiveness of the proposed approach. Operational modal identification based on the proposed approach and procedure is conducted to identify the modal parameters of a simulated spatial frame structure under the ambient excitations. The proposed approach is further used for operational modal identification of a seven-storey shear type steel frame structure in the laboratory and a real footbridge under ambient vibrations to verify the accuracy and performance. The modal identification results from both numerical simulations and experimental validations demonstrate that the proposed approach can effectively and accurately decompose the vibration responses and identify the structural modal parameters under operational conditions.
