Shunyao Cai

Steel strand cables, particularly in their anchorage zones, are simultaneously exposed to corrosive environments and subjected to tensile-bending coupling loads. Field observations of bridge cable failures indicate that they are primarily governed by the stress concentration characteristics of internally corroded steel strands under this complex stress state. Consequently, it is critical to investigate the corrosion-induced stress concentration characteristics of steel strands in bridge cables under realistic tension and bending coupling loads. An accelerated salt spray corrosion test was designed and conducted on steel strands in bridge cables. The mass loss rates of steel strands were analyzed to quantify varying degrees of corrosion. The morphological characteristics of corrosion pits on steel strands at different corrosion levels were observed and analyzed using a three-dimensional laser scanner. Based on the scanned data, the probability density functions for pit depth were fitted for each corrosion condition. Subsequently, a refined numerical methodology was developed to model corroded steel strands under tension-bending coupling loads. This methodology was utilized to perform a parametric study investigating the stress concentration characteristics at corrosion pits with different spatial dimensions of steel strands. The research results indicate that the mass loss rate of the steel strands increases nonlinearly with increasing corrosion duration. The depth of corrosion pits on the steel strands follows a Gaussian distribution across all investigated corrosion levels. The stress concentration factor of the corroded steel strands exhibits a significant linear correlation with the corrosion pit geometry. Specifically, the stress concentration factor decreases linearly with increasing corrosion pit length, but increases linearly with both corrosion pit width and depth. Quantitatively, a 0.1 mm increase in corrosion pit length, width, and depth results in decreases in the stress concentration factor by 0.062, increases by 0.036, and increases by 0.062, respectively.

Optimizing cable forces in cable-stayed bridges is challenging due to structural nonlinearity and the limitations of traditional methods, which often focus on isolated performance indicators. This study proposes an integrated framework combining Gaussian process regression (GPR) with an enhanced whale optimization algorithm improved by the Salp Swarm Algorithm (EWOSSA). GPR is first used to model the nonlinear relationship between cable forces and structural responses. The EWOSSA then efficiently optimizes the GPR-based model to identify optimal cable forces. A case study on a cable-stayed bridge with a 2 × 145 m main spans demonstrates the effectiveness of the proposed approach. Compared with conventional methods such as the internal-force equilibrium and zero-displacement methods, the EWOSSA-GPR framework achieves superior performance across multiple structural metrics. It ensures a more uniform cable force distribution, reduces girder displacements, and improves bending moment profiles, offering a comprehensive solution for optimal structural performance in cable-stayed bridges.

Construction machinery operations are intrinsically linked to critical societal challenges, including safety risks and carbon emissions. In response to the high incidence of fatal accidents during installation and dismantling phases, the Chinese government has officially promoted integrated installation–dismantling services to enhance construction safety since 2023. However, the economic viability of this policy for leasing companies remains largely underexplored. To address this gap, this paper develops a leasing-oriented closed-loop construction machinery supply chain model that incorporates integrated installation–dismantling services under an industrial internet platform. The study first compares and analyzes the product leasing demand, installation and dismantling demand, and supply chain profits under both centralized and decentralized decision-making scenarios. Based on these analyses, a profit–cost sharing joint contract is designed to coordinate the supply chain. Furthermore, the interrelationships among key parameters are examined through a sensitivity analysis and numerical simulation. The results reveal that enhancing leasing information services increases both the demand for construction machinery and the platform’s operating costs. These costs are positively correlated with the product’s selling price, leading to higher purchasing costs for lessees. Similarly, improving information services for installation and dismantling raises the platform’s operating costs and enhances service levels, which in turn increases installation and dismantling costs for lessees. The findings demonstrate that within a certain range of cost-sharing and leasing-sharing proportional coefficients, the joint contract enables the supply chain to achieve Pareto optimization. This approach simultaneously alleviates economic pressure on lessees, improves construction safety, and promotes the integration of installation and dismantling services.

Orthotropic steel decks (OSD) are widely used in the bridge industry, but they are susceptible to fatigue cracking due to various factors such as weld defects, overload traffic, and complicated welded joint configurations. Traditional crack detection methods rely mainly on visual inspection, which is time-consuming and prone to errors. Recently, many crack detection methods based on machine vision have been introduced. However, accurately and effectively detecting fatigue cracks of OSD remains challenging due to the indistinct nature of these cracks compared to the background and their susceptibility to various types of noise. To address these problems, this research proposes an improved Mask Region-based Convolutional Neural Network (Mask R-CNN). The proposed model enhances the feature extraction ability of the original Mask R-CNN by introducing a Convolutional Block Attention Module (CBAM) and Path Augmentation Feature Pyramid Network (PAFPN) into the backbone network. Additionally, a morphological closing operation is performed on the extracted fatigue cracks to address the problem of disjoint cracks. On the basis of crack mask closure, the pixel-wise quantization of cracks was achieved using relevant OpenCV functions. Evaluation on a crack dataset of OSD shows that the proposed model outperforms other methods with precision, recall, average precision (AP), and F1 score of 83.98%, 84.44%, 77.9%, and 84.21%, respectively. In conclusion, the experiment validates that the proposed model can effectively detect, segment, and quantify fatigue cracks of OSD.

Promoting sustainable production and consumption practices in the construction machinery industry is crucial for achieving energy savings and reducing carbon emissions. However, there is a lack of targeted studies addressing the challenge of scaling up leasing and recycling while maximizing economic benefits for enterprises. To fill this gap, this paper presents a lease-oriented closed-loop supply chain model that incorporates a carbon tax policy to investigate the impact of the carbon tax rate and consumer preferences for remanufactured products on the supply chain and introduces a leasing compensation–cost apportioning combined contract to achieve supply chain coordination in the construction machinery sector. The model considers differential selling and leasing prices for new and remanufactured products, as well as the recovery rate, under both centralized and decentralized decision-making approaches. The study explores the interrelationships between various parameters through sensitivity analysis and numerical simulation. The results demonstrate that within a certain range of the cost apportioning proportional coefficient and leasing compensation proportional coefficient, the combined contract can lead the supply chain to achieve Pareto optimality. As the carbon tax rate increases, it was observed that the profits for all parties in the supply chain tend to decrease. However, due to the increased demand for remanufactured products, the product recovery rate improves, resulting in a reduction in total carbon emissions in the closed-loop supply chain of construction machinery. Moreover, the profits of all parties and the total supply chain profits initially decrease and then increase with an increasing preference coefficient for remanufactured products among consumers. By leveraging these factors and adopting effective strategies, such as enhancing consumer recognition of remanufactured products and optimizing pricing and cost allocation, it is indeed possible for the profits of all parties and the total profits in the supply chain to surpass the initial values, even in the face of increasing carbon tax rates. This demonstrates the potential for aligning sustainability objectives with economic benefits in the construction machinery industry.

Concrete-encased concrete-filled steel tube (CFST) is an innovative composite member that consists of the inner CFST components and an outer reinforcement concrete encasement. One increasing application scenario of concrete-encased CFST is acting as the arch rib in large-span arch bridges that are generally constructed through a three-stage procedure. Consequently, significant two-stage initial stresses in the steel tube and inner CFST are commonly generated, which might influence the structural behavior of the arch ribs. This paper aimed to study the effect of the high-level two-stage initial stresses on the sectional performance of concrete-encased CFST arch subject to axial compression. Accordingly, six concrete-encased CFST stub column specimens with two-stage initial stresses, as well as 12 reference specimens with preload on steel tube or CFST only, were tested and analyzed through a refined finite-element model. The simplified prediction formula for ultimate compressive strength of concrete-encased CFST stub column with different initial stress scenarios were also established. The results show that the column specimens with two-stage initial stresses generally failed due to the premature crushing of outer concrete caused by the pronounced outward swelling effect from the yielded and locally buckled inner steel tube. The yielding and local buckling of steel tube were fairly hastened by its initial stress. Therefore, it is recommended that the initial stress ratio of steel tube should be limited within a reasonable value in the design. The specific value is around 0.6 within the scope of the structural parameters of this study.