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.
Currently, new robotic technology is widely used in pipeline maintenance and inspection. Robots made of soft materials have been developed and used in pipeline inspection to overcome the limitations of rigid in-pipe robots and improve maneuverability. The turning control of soft robots in pipelines is a great challenge, owing to the various specifications and branches of pipelines. A soft robot for pipes with small diameters was developed in this study to solve this problem using a kinematic model. Based on this model, the flexible turning strategy of robots in T-branch pipes was established. Finally, the effectiveness and accuracy of the turning strategy were verified using experiments. The proposed turning strategy can effectively improve the mobility and intelligence of soft in-pipe robots in T-branch pipes.
The determination of reaction rate coefficients and kinetic isotope effects (KIE) and measurement of cross-section are indispensable to the study of combustion, atmospheric, and interstellar chemical kinetics and dynamics. However, these parameters cannot be accurately and efficiently calculated by common theoretical methods such as quantum scattering, transition state theory (TST), and quasi-classical trajectory. In recent years, the ring-polymer molecular dynamics (RPMD) theory has been successfully applied for the investigation of gas-phase bimolecular reaction dynamics. Driven by modern advances, RPMD can also be implemented in many new applications. This work will review the present challenges of and recent advances in RPMD.
Materials data are multi-source, heterogeneous, and high-dimensional. Acquiring diverse and complex materials data as well as establishing a dedicated database for materials genome engineering (MGE) is the foundation for realizing data-driven new materials design. Herein, the materials genome database platform is introduced in terms of its system architecture, implementation, and deployment on a supercomputer. It is based on several core technologies, such as normalized representation of materials data, machine-learning modeling and model cross-domain deployment, machine learning under data privacy protection, and a materials database to a knowledge base using a knowledge graph. Finally, based on an anti-perovskite negative expansion material as an example, the entire application process of the MGE database platform from data curation to machine learning modeling followed by inverse design, in addition to a final experimental validation are discussed comprehensively herein.
Regression modeling on small-sample scattered data poses certain challenges. In this study, the Gaussian process is used to model regression, and maximum likelihood estimation is performed to learn the hyperparameters of the kernel function. The regression results, i.e., the mean and variance of the objective function, are calculated and predicted from the posterior. Combining the results with the multi-objective optimization of variance, the uncertainty of material reverse design can be estimated. Experimental verifications are conducted on 1215MS non-quenched and tempered steel and three-point bending concrete datasets. The results show that for the three-point bending concrete, 50% of the experimental data are within the 95% confidence interval of the prediction, and the Gaussian process regression (GPR) model can measure the uncertainty of the scattered small-sample data more effectively and yield reasonable predictions. For the 1215MS dataset, a non-dominated genetic algorithm with an elite strategy is used to perform multi-objective optimization based on the GPR model. The mechanical properties of the material and the corresponding variance are used as optimization objectives, and the optimal mechanical properties are considered while considering the effect of uncertainties on the experimental results. The optimal Pareto solution set is obtained, which is subsequently used as candidate points for the next experiment to assist material design and preparation optimization.
Determining how to combine material-domain knowledge with the machine learning method is an urgent problem in materials intelligence. As an efficient knowledge-organization method, knowledge graphs (KGs) can effectively represent, organize, and reasoning material-domain knowledge so as to improve the intelligence level of machine-learning algorithms for materials. In this paper, we study natural language processing (NLP)-based knowledge-acquisition methods for materials and propose a joint extraction method comprising the material entity relationship based on bidirectional-gated recurrent unit-graph neural network-conditional random field (Bi-GRU-GNN-CRF) and a material-processing knowledge-extraction method based on the improved TextRank algorithm. Using the proposed knowledge-acquisition method, we acquire material-domain knowledge such as material entities, relationships, and technological processes from patents, papers, and other types of texts. The experimental results show that the proposed knowledge acquisition method has good accuracy and recall, which can effectively improve the knowledge coverage of the material KGs. The knowledge coverage of the material KGs constructed based on proposed method reaches 80%, which provides more comprehensive knowledge support for materials research and development. We also construct the domain KGs of special non-modulated steel, an aluminum matrix composite material, and a thermal-barrier ceramic-coating material, and the results further verify the potential of using material knowledge maps in materials research and development.
Surface detachment is one of the main defects of the Shanxi Academy walls, and it can lead to the peeling of cement mortar plaster and the weathering of interior masonry structures. Thus, it is necessary to evaluate its severity before conducting detailed conservation and restoration work. In this study, manual percussion was first performed for the preliminary detection of surface detachment defect. Next, two non-destructive testing techniques, i.e. phased-array ultrasonic imaging and infrared thermal imaging, were combined to measure the scope and area of distribution in detail. The results showed that (1) all methods could reflect the surface detachment distribution to different degrees, (2) data obtained using different non-destructive techniques were comparable to some minor errors correlated with defect types and environmental factors, (3) the detachment of cement mortar plaster from the wall surface was generally severe, and its defect rating could be divided into four specific grades. The research results are useful for detecting and evaluating surface detachment in cultural heritage buildings.
The construction of first-class disciplines is the core of the construction of first-class universities. The paper explains the construction opportunities, construction ideas, construction paths and construction priorities in the field of “Five Golden Flowers” of science and engineering in the “14th Five-Year Plan” period of Shanghai University, and further discusses the three important relationships that need to be considered and handled in promoting the construction of “Double First-Class”.
High-temperature Superconducting Magnetic Energy Storage system has the advantages of high power density, fast response and long life. It has potential application prospects in the fields of new energy grids and new energy electric ships. Due to the superior current-carrying capacity, high operating temperature, and relatively low fabrication cost of second-generation high-temperature superconducting tapes, they are widely used in superconducting energy storage devices. In this paper, based on the introduction of YBCO high temperature superconducting tape, the performance requirements of energy storage devices is analyzed, and a specific case analysis has been carried out in combination with the design of 10 MJ energy storage magnets.
Turbine blades are the core hot-end components of gas turbines. The research, development, and manufacturing of turbine blades are vital to the heavy industry at the national level. This paper summarizes the research and development progress of gas turbine blade preparation technologies. Additionally, for research topics investigated by the research group, the research progress in related fields is introduced based on the directional solidification of blades, electromagnetic field regulation on microstructure technology, preparation of ceramic core, dimensional accuracy control, pure smelting of superalloy, and additive manufacturing technology. Important technologies to be prioritized are highlighted.
Dirac semimetals have received extensive attention both experimentally and theoretically because of their novel electronic structures and transport properties. Topological Dirac semimetals have symmetry-protected Dirac points near the Fermi level, where the Dirac points are due to the formation of band inversions between the conduction and valence bands in solids. In this review, we introduce centrosymmetric topological Dirac semimetals and a new three-dimensional noncentrosymmetric topological Dirac semimetal. Through the analysis of crystal symmetry and energy band symmetry, one finds that crystals with C$_{\rm 4v}$ or C$_{\rm 6v}$ point groups can realize noncentrosymmetric topological Dirac semimetals. BiPd$_{2}$O$_{4}$ crystal with C$_{\rm 4v}$ point group is theoretically predicted to be noncentrosymmetric Dirac semimetals with topological type Ⅱ Dirac points on the C$_{\rm 4v}$ rotation axis. In addition, SrHgPb crystal and LiZnSb$_{x}$Bi$_{1-x}$ alloys with C$_{\rm 6v}$ point group are predicted to realize topological semimetals in which Dirac and Weyl points coexist, and the appearance and location of Weyl points in LiZnSb$_{x}$Bi$_{1-x}$ alloys can be regulated by the alloy concentration $x$. Compared with centrosymmetric topological Dirac semimetals, noncentrosymmetric topological Dirac semimetals have potential applications in nonlinear optics and nonlinear Hall transport due to the broken inversion symmetry.
The buffer layer in a deep geological repository used in the disposal of high-level radioactive waste (HLW) is composed of bentonite blocks that are compressed with bentonite powders, and their strength may show anisotropic characteristics. In this study, the tensile strength of compacted Gaomiaozi (Gaomiaozi, China) bentonite under different conditions was measured by direct tensile tests, and the effects of dry density and moisture content on the anisotropy of tensile strength were analyzed and discussed. The results showed that the tensile strength initially increased and then decreased with increasing water content, during which a peak value was observed. The tensile strength also increased with increasing dry density. When the direction of the tensile force was perpendicular to that of the compaction force, the tensile strength was greater than when the direction of the tensile force was parallel to that of the compaction force. In addition, under the same water content, the degree of anisotropy of tensile strength increased with increasing dry density. This could be explained by the fact that montmorillonite laminae tend to be arranged in a direction perpendicular to that of the compaction force, leading to significant anisotropy. These results can provide a reference for assessing the tensile strength characteristics of bentonite blocks in transportation and hoisting processes in the disposal of HLW in China.
