Large-scale modern exhibition venues are more sensitive to uneven foundation settlements, where the spatial distribution of the compressive modulus of the bearing layer is essential in controlling foundation deformations. Conventional engineering survey boreholes provide only a small number of precise compressive modulus geotechnical test values, whereas in-situ testing can provide numerous random cone penetration values. To integrate the data of indoor and in-situ tests, a Bayesian spatial interpolation method of compression modulus is proposed in this study. Our research was conducted as follows. Based on the data accuracy of geotechnical engineering investigation, test data were divided into hard and soft data. A spatial random function was then used to describe the spatial variability of the compression modulus. Next, maximum entropy theory was applied to analyze the uncertainty of the soft data. Based on Bayesian theory, a random field interpolation method was then established to estimate the posterior distribution of the compression modulus of unknown points. Finally, to verify the effectiveness of the proposed method, a Bayesian spatial interpolation method was applied to the spatial variability analysis of the compressive modulus of silty clay in the shallow bearing layer ②$_1$ of Shanghai National Convention and Exhibition Center. Compared with the ordinary Kriging interpolation method, the proposed Bayesian method can integrate multi-source survey data for spatial interpolation with greater accuracy.
Design changes represent one of the main risk factors that affects the performance and safety of prefabricated building projects. Multi-stakeholder participation in prefabricated component supply chains involves a complex and changeable system, and information integration of each participant is difficult. This in turn makes it difficult for the project to achieve systematic and effective design change risk management and control. Based on the induction of design change risks and risk-causing factors, a method that combines a structural equation and multi-agent simulation model is proposed in this study. Management strategies are first proposed based on the identification of risk-inducing factors. The actual operational effects of these management strategies are then simulated to achieve an optimal allocation of resources. Based on a prefabricated construction project in Nanxiang Town, Shanghai, an ontology instance library is established, and the proposed management strategies by empirical analysis of the structural equation model are simulated through the multi-agent simulation model. The effects of the strategies on project cost and time performance are evaluated, and the feasibility and effectiveness of the model are verified.
The literature related to molecular design and performance prediction of high-temperature resistant transparent polyimide at home and abroad in recent years was studied, and the common molecular designs of high-temperature resistant transparent polyimide: introduction of trifluoromethyl, alicyclic structure, non-coplanar structure, bulk side groups, and inorganic materials were summarized, and the research on the application of molecular dynamics simulation and machine learning methods in the performance prediction of polyimide was analyzed. Finally, the molecular design and performance prediction of high-temperature resistant transparent polyimide were summarized and prospected.
In this paper, the impact of corporate social responsibility (CSR) goodwill on market demand was considered. For the cases of Nash non-cooperative game, manufacturer-led Stackelberg game, retailer-led Stackelberg game, and cooperative game, differential game models were constructed and the optimal level of CSR effort for the manufacturer and the retailer, the optimal trajectory of CSR goodwill, and the optimal value of profit was studied. Also, the correctness of the theoretical derivations was verified through numerical simulations, and some important results were obtained. First, the optimal subsidy rate of CSR cost is related to marginal profit and is unaffected by other factors. Second, compared with the Nash non-cooperative game, in the case of Stackelberg game, the leader subsidizes the CSR cost of the follower when the marginal profit of the leader is greater than half the marginal profit of the follower. Moreover, the optimal level of CSR effort for the leader remains unchanged, while the optimal level of CSR effort for the follower increases, so the profits improve for both the manufacturer and the retailer. Third, compared to the Stackelberg game, in the case of cooperative games, the optimal level of CSR effort improves for both the manufacturer and the retailer, and the profit also increases for the supply chain system.
Because of the diverse compositions of asphalt pavement damage, multiple cases of damage that are inspected using the same pavement condition index (PCI) may yield different damage combinations. When multiple types of damage coexist but the degree of damage is similar, obtaining targeted maintenance measures with PCI and determining the predominant damage (i.e., most severe road damage with the maximum deduction value) are challenging. Therefore, this study considers PCI analysis and an existing preventive maintenance decision-making method to clarify those sections in which the predominant damage was not well-targeted during inspection and proposes a supplementary approach to make more appropriate conservation decisions. Based on detection and maintenance data of urban roads in Shanghai from over the past five years, a sequential clustering method is used to classify road sections according to their PCI levels. The composition of and difference in pavement damage at different levels are analyzed. Then, for those sections with multiple cases of damage and no significant damage differences, road sections that historically reflect proper preventive maintenance are then selected based on whether effective preventive maintenance can be implemented. Finally, two back propagation (BP) neural network models for preventive maintenance decisions are established and compared based on the effective maintenance road sections. The main differences between the two models are the compositions of pavement damage. The results showed that when the PCI levels were high (84.4~93.0 points), the degrees of damage were very similar and the predominant damage was not represented. Of the two BP neural network models, Model 2,which considered multiple damage components, showed a higher decision accuracy. Specifically, its decision accuracy with the test set reached 86.20%. This was significantly better than that of Model 1 (58.50%), which considered only the predominant damage. Combining the BP neural network and traditional decision tree method can help to optimize decision-making processes related to asphalt pavement and improve the selection of maintenance measures.
To study the nonlinear deformation problem of functionally graded plates, this study uses the S-R decomposition theorem combined with the updated comoving coordinate system method and meshless Galerkin method to derive a discrete equation for solving three-dimensional geometric nonlinear problems. The meshless method is programmed by MATLAB. The nonlinear bending problem of the functionally graded plate is first solved, and the effects of the volume fraction index and width-thickness ration on the bending of plates are studied. Results are compared with existing results, and the rationality of solving the large deformation problem of functionally graded plates using the three-dimensional S-R meshless method is verified.
To conduct an experimental study of the flexural theory of hybrid steel and fibre reinforced plastic (FRP) reinforced beams, the finite element software ABAQUS was used to model and nonlinearly analyse existing hybrid FRP/steel reinforced test beams. The effects of FRP bar type, concrete strength, and the equivalent reinforcement ratio on the bending performance of hybrid FRP/steel reinforced beams were then studied. Finite element results revealed that the stress expression of FRP bars could be obtained and a formula for the bearing capacity of hybrid FRP reinforced beams could be derived. Finally, experimental data were used to verify the correctness of the formula. Results showed that the equivalent reinforcement ratio had the most significant effect on the bearing capacity and deformation performance of hybrid FRP bars beams, followed by FRP bar type. The strength of concrete had a certain effect on the bearing capacity but had little effect on stiffness. Based on the stress expression of FRP bars in this study, the flexural capacity of hybrid FRP reinforced beams were accurately calculated.
Joint-stock commercial bank has been a principal form of the contemporary commercial bank system. This paper evaluates efficiency of China’s 10 joint-stock commercial banks from 1997 to 2004 using the data envelopment analysis (DEA). The Tobit model is applied to analyze the factors affecting efficiency. The result shows that asset profitability is positively related to efficiency of these banks, while the ratio between loans and deposits is negatively related to the efficiency. Both relations are significant. In order to improve efficiency, banks should improve asset quality and maintain balance between profitability and liquidity. Moreover, performance of listed banks is better than that of non-listed banks in terms of efficiency. Therefore it is beneficial to encourage non-listed banks to go public.
