To enhance the accuracy and efficiency of bridge damage identification, a novel data-driven damage identification method was proposed. First, convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction. The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance. The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge. The numerical simulation results show that the identification errors remain within 2.9% for six single-damage cases and within 3.1% for four double-damage cases. The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%, the method accurately identifies damage at different cable locations using only sensors installed on the main girder, achieving identification accuracies above 95.8% in all cases. The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

According to news reports on severe earthquakes since 2008, a total of 51 cases with magnitudes of 6.0 or above were analyzed, and 14 frequently occurring secondary disasters were identified. A disaster chain model was developed using principles from complex network theory. The vulnerability and risk level of each edge in this model were calculated, and high-risk edges and disaster chains were identified. The analysis reveals that the edge “floods→building collapses” has the highest vulnerability. Implementing measures to mitigate this edge is crucial for delaying the spread of secondary disasters. The highest risk is associated with the edge “building collapses→casualties, ” and increased risks are also identified for chains such as “earthquake→building collapses→casualties, ” “earthquake→landslides and debris flows→dammed lakes, ” and “dammed lakes→floods→building collapses.” Following an earthquake, the prompt implementation of measures is crucial to effectively disrupt these chains and minimize the damage from secondary disasters.

Taking a three-cable flexible photovoltaic(PV)support structure as the research subject, a finite element model was established. Utilizing a full-order flutter analysis method, the flutter critical wind speed and flutter frequency of the flexible PV support structure at a tilt angle of 0° were calculated. The results showed good agreement with wind tunnel test data. Further analysis examined the pretension effects in the load-bearing and stabilizing cables on the natural frequency and flutter critical wind speed of the flexible PV support structure. The research findings indicate increasing the pretension in the load-bearing cables significantly raises the natural frequencies of the first four modes. Specifically, as the pretension in the load-bearing cables increases from 22 to 102 kN, the flutter critical wind speed rises from 17.1 to 21.6 m/s. By contrast, the pretension in the stabilizing cable has a smaller effect on the natural frequency and flutter critical wind speed of the flexible PV support structure. When the pretension in the stabilizing cable increased from 22 to 102 kN, the flutter critical wind speed increased from 17.1 to 17.7 m/s. For wind-resistant design of flexible PV support structures, it is recommended to prioritize increasing the pretension in the load-bearing cables to enhance the structural flutter performance.

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan. Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering, aiming to improve structural performance evaluation, damage diagnosis, and capacity prediction. This review delves into the framework of predictive maintenance and examines various existing solutions, focusing on critical areas such as data acquisition, condition monitoring, damage prognosis, and maintenance planning. Results from real-world applications of predictive maintenance in civil engineering, covering high-rise structures, deep foundation pits, and other infrastructure, are presented. The challenges of implementing predictive maintenance in civil engineering structures under current technology, such as model interpretability of data-driven methods and standards for predictive maintenance, are explored. Future research prospects within this area are also discussed.

To enhance the serviceability of steel bridge deck pavement(SBDP)in high-temperature and rainy regions, a concept of rigid bottom and flexible top was summarized using engineering practices, which led to the proposal of a three-layer ultra-high-performance pavement(UHPP). The high-temperature rutting resistance and wet-weather skid resistance of UHPP were evaluated through composite structure tests. The internal temperature distribution within the pavement under typical high-temperature conditions was analyzed using a temperature field model. Additionally, a temperature-stress coupling model was employed to investigate the key load positions and stress response characteristics of the UHPP. The results indicate that compared with the traditional guss asphalt + stone mastic asphalt structure, the dynamic stability of the UHPP composite structure can be improved by up to 20.4%. Even under cyclic loading, UHPP still exhibits superior surface skid resistance compared to two traditional SBDPs. The thickness composition of UHPP significantly impacts its rutting resistance and skid resistance. UHPP exhibits relatively low tensile stress but higher shear stress levels, with the highest shear stress occurring between the UHPP and the steel plate. This suggests that the potential risk of damage for UHPP primarily lies within the interlayer of the pavement. Based on engineering examples, introducing interlayer gravel and optimizing the amount of bonding layer are advised to ensure that UHPP possesses sufficient interlayer shear resistance.

Through a self-developed model test system, the mechanical properties of silt and the deformation characteristics of airport runways were investigated during the period of subgrade wetting. Based on the test results, the reliability of the numerical simulation results was verified. Numerical models with different sizes were established. Under the same cushion parameter and loading width ranges, the effects of the cushion parameters and loading conditions on the mechanical responses of the cushion before and after subgrade wetting were analyzed. The results show that the internal friction angles of silt with different wetting degrees are approximately 34°. The cohesion is from 8 to 44 kPa, and the elastic modulus is from 15 to 34 MPa. Before and after subgrade wetting, the variation rates of the cushion horizontal tensile stresses with the same cushion parameters and loading width ranges are different under the influence of boundary effects. After subgrade wetting, the difference in the variation rates of the cushion horizontal tensile stresses under the same cushion parameter range decreases compared with that before subgrade wetting; however, this difference increases under the same loading width range. Before and after subgrade wetting, the influence of the boundary effect on the mechanical response evaluation of the cushion is not beneficial for optimizing the pavement design parameters. When the cushion thickness is more than 0.25 m, the influence of the boundary effect can be disregarded.

Polyamide/polyethylene(PA/PE)microplastics were injected into soil containing sulfamethoxazole(SMX)to investigate their combined effects on SMX removal, soil enzyme activity, and microbial communities. The results show that both PA and PE transiently increase SMX removal and inhibite the stimulation of microbial species diversity by SMX. The effect of PE is more significant. Meanwhile, PE combined with SMX increases the relative abundances of Actinobacteria and Pseudomonas, while PA combined with SMX decreases the relative abundances of Nocardioides and Streptomyces. In addition, PA/PE combined with SMX can increase dehydrogenase, urease, ammonia monooxygenase, and nitrate reductase activities in the soil while inhibiting the activity of laccase. Compared with PA combined with SMX, the activities of dehydrogenase, urease, ammonia monooxygenase, and laccase of PE combined with SMX increase by 9.82%, 10.41%, 8.07%, and 5.47%, while the activities of nitrate reductase and neutral phosphatase decrease by 1.47% and 6.78%.

To accurately analyze the impact of casting pores in steel, high-resolution 3D X-ray tomography technology was used to gather detailed statistical information about micropores. These micropores were classified as gas, shrinkage, and gas-shrinkage pores depending on their formation origin and morphology. Clustering tendencies and affinity parameters were defined to characterize the spatial correlations among these three types of pores. The 3D data from X-ray tomography scans were then integrated into finite element analysis(FEA)software to predict how micropore shape, size, and distribution influence stress distribution within the material. The results show that certain inflection points with small local radii within the cast pores are major contributors to stress concentration. Therefore, cast pores cannot be simply modeled as ideal spherical pores. The sphericity and volume of pores have a significant impact on the stress concentration of the model. Specifically, lower sphericity and larger pore volumes result in higher stress concentrations. Moreover, the internal pores of steel castings exhibit specific global distribution characteristics. Pores located on the surface of the specimen lead to significantly higher stress concentrations compared to those located inside the specimen.

Considering that copper mine tailings(CMTs)are commonly mixed with ordinary Portland cement, fly ash(FA), and kaolin to produce geopolymers, to make full use of CMTs, the properties of geopolymers manufactured under different material mass ratios and curing methods(standard curing, water bath curing, and 60 ℃ curing)are evaluated with significantly increased dosage of CMTs. Porosity and unconfined compressive strength tests, X-ray diffraction, field emission scanning electron microscopy, and energy dispersive spectroscopy are used to determine the physical and mechanical properties, microstructure, and mineral composition of geopolymers. Finally, costs and CO₂ emissions of specimens with different material mass ratios during the preparation processes are compared. The results show that during the geopolymerization of low-calcium materials, various geopolymer gels, including calcium silicate, calcium silicoaluminate, and mainly sodium silicoaluminate gels, coexist. The solid waste, cost, and carbon dioxide emission reductions can reach 100%, 166.3 yuan/t, and 73.3 kg/t, respectively. Under a curing condition of 60 ℃, the sample with a CMTs mass fraction of 70% and an FA mass fraction of 30% meets the requirements of porosity, compressive strength. The resource utilization of CMT and FA is realized in a more economical way.

To tackle the problem of inaccurate short-term bus load prediction, especially during holidays, a Transformer-based scheme with tailored architectural enhancements is proposed. First, the input data are clustered to reduce complexity and capture inherent characteristics more effectively. Gated residual connections are then employed to selectively propagate salient features across layers, while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data. Ultimately, a pre-trained structure is incorporated to reduce computational complexity. Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00% consistently across all buses evaluated, and the fitting effect of holiday load curves is outstanding. Meanwhile, the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%. The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions, making it better adapted to real-world prediction scenarios.

To characterize m-weak group inverses, several algebraic methods are used, such as the use of idempotents, one-side principal ideals, and units. Consider an element a within a unitary ring that possesses Drazin invertibility and an involution. This paper begins by outlining the conditions necessary for the existence of the m-weak group inverse of a. Moreover, it explores the criteria under which a can be considered pseudo core invertible and weak group invertible. In the context of a weak proper *-ring, it is proved that a is weak group invertible if, and only if, a^D can serve as the weak group inverse of au, where u represents a specially invertible element closely associated with a^D. The paper also introduces a counterexample to illustrate that a^D cannot universally serve as the pseudo core inverse of another element. This distinction underscores the nuanced differences between pseudo core inverses and weak group inverses. Ultimately, the discussion expands to include the commuting properties of weak group inverses, extending these considerations to m-weak group inverses. Several new conditions on commuting properties of generalized inverses are given. These results show that pseudo core inverses, weak group inverses, and m-weak group inverses are not only closely linked but also have significant differences that set them apart.

Aiming to identify policy topics and their evolutionary logic that enhance the digital and green development(dual development)of traditional manufacturing enterprises, address weaknesses in current policies, and provide resources for refining dual development policies, a total of 15 954 dual development-related policies issued by national and various departmental authorities in China from January 2000 to August 2023 were analyzed. Based on topic modeling techniques and the policy modeling consistency(PMC)framework, the evolution of policy topics was visualized, and a dynamic assessment of the policies was conducted. The results show that the digital and green development policy framework is progressively refined, and the governance philosophy shifts from a “regulatory government” paradigm to a “service-oriented government”. The support pattern evolves from “dispersed matching” to “integrated symbiosis”. However, there are still significant deficiencies in departmental cooperation, balanced measures, coordinated links, and multi-stakeholder participation. Future policy improvements should, therefore, focus on guiding multi-stakeholder participation, enhancing public demand orientation, and addressing the entire value chain. These steps aim to create an open and shared digital industry ecosystem to promote the coordinated dual development of traditional manufacturing enterprises.