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Corrosion thinning reduces the effective cross-sectional areas of steel structures and degrades their mechanical properties. This study aims to investigate the relationship between the corrosion thinning of carbon steel for transmission towers and the degradation of its mechanical properties.

A macroscopic finite element model of a transmission tower was established and then combined with the corrosion thinning and mechanical properties of Q355 steel in different test periods measured in neutral salt spray, SO₂ atmosphere and wet heat environments to conduct a finite element simulation of a transmission tower with different corrosion thinning of Q355 steel.

When the residual thickness of the tower leg angle was reduced to 4.03 mm, the maximum stress solved in the simulation exceeded the yield strength, with the tower already at risk of collapse owing to corrosion failure under extreme conditions of basic wind speed.

This study innovatively utilises transmission tower finite element models and experimental data from mechanical degradation experiments to quantify the relationship between corrosion thinning and the mechanical properties of Q355 steel, ensuring the effective assessment of the mechanical properties of corroded transmission towers.

The purpose of this paper is to investigate the effect of plasma electrolytic oxidation (PEO) coatings and sealed PEO coatings on the corrosion resistance and cytocompatibility of a novel Mg-1Zn-0.45Ca alloy in simulated body fluid (SBF).

The microstructure, corrosion resistance and cytocompatibility of PEO coatings and phosphate conversion-treated PEO coatings were investigated and was compared with the bare Mg alloy.

The hot-extruded Mg-Zn-Ca alloy exhibit inhomogeneous microstructure and suffered from localized corrosion in the SBF. The PEO coating after phosphate conversion treatment offers enhanced protectiveness to the Mg alloy within an immersion period of up to 60 days, which is significantly improved compared with the performance of the PEO-coated Mg alloy, but the cytocompatibility was slightly decreased.

This work offers new perspective in balancing the protectiveness and cytocompatibility of bio-materials.

Additive lamination manufacturing (ALM), as a novel additive manufacturing technology, builds up the geometry via the lamination of fiber-reinforced polymer (FRP) fabric laterally, rendering it suitable for fabricating large-scale Stay-in-Place concrete formwork. This paper aims to investigate the control parameters and structure performance of ALM and assess its application for the fabrication of large-scale concrete formwork.

Based on previous feasibility studies, this research systematically investigates the control and material parameters that influence horizontal and vertical extrusion speeds, as well as the overall quality of ALM. Once the system parameters are established, a series of prototypes are fabricated and tested to validate the tensile strength of the formwork and its reinforcement capabilities. In addition, this study assesses the potential geometric freedom and implementation constraints of ALM.

This research identifies the essential control parameters for path planning in ALM and examines their impact on fabrication. In addition, this paper evaluates ALM’s strengths and limitations in producing concrete formwork for large-scale concrete structures, comparing these to industry benchmarks.

A critical challenge in additive manufacturing lies in its scalability and compatibility with existing construction processes. In comparison to concrete, FRP offers advantages such as being lighter, easier to handle and providing surface protection and reinforcement. These qualities make FRP superior for formwork and compatible with existing building standards. Despite its advantages and potential, the current path planning and control model in 3D printing do not apply to ALM due to its novel build-up process. Also, the performance of fabricated parts as part of integrated large-scale structures is yet to be studied.

Titanium alloys and composites have proven to contain desirable properties for use at elevated temperatures. One such material is the Ti750 composite, which can be used at temperatures up to 750°C for a brief period. This paper aims the microstructure, phase compositions, apparent porosity and hardness of both sintered and heat-treated TiC reinforced Ti750 composites for consideration in aircraft engine design.

The fabrication of TiC-reinforced Ti750 composites was achieved through spark plasma sintering (SPS). To analyze the microstructure and X-ray diffraction, a scanning electron microscope (SEM) with model number S-3400N and a D8 advance model machine were used, respectively. The microhardness of the samples was measured using a Vickers hardness tester with model HV-1000. The research incorporated three solid solution treatments: 975°C/3 h/AC, 1,010°C/3 h/AC and 1,025°C/3 h/AC, along with a solid-solution aging treatment at 1,010°C/3 h/AC + 750°C/8 h/AC. Additionally, oxidation analysis was conducted on the samples.

The microstructures contained enhanced TiC and Ti5Si3 phases in the near a-Ti matrix. The microhardness of the sintered composite was over twice that of the matrix alloy, and its porosity was reduced by about 0.35%. The sample treated at 1,010°C/3 h/AC had the highest enhanced peaks and microhardness of 1,277.1 HV. After oxidation at 800°C for 100 h, the accumulated weight of the solid solution composite at 1,010 °C/3 h/AC was the lowest (3.0 mg.cm^-2). The surface microstructure contained oxides of TiO2 and a spalling white area containing a small amount of Al₂O₃ and SiO₂.

There is limited research on Ti-Al-Sn-Zr-Mo-Si-based TMCs using a combination of the SPS method. This study used SiCp as a reinforcement for the Ti750 matrix alloy. The consolidation of SiCp and Ti750 powders using the SPS method, heat treatment of the resulting TiC reinforced Ti750 composites and study of the microstructure and properties of the composites are not found in literature or under consideration for publication in any media.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.

This paper aims to address the challenges of the capacitor tower maintenance robot during bolt tightening in high-voltage substations, including difficulties in bolt positioning due to tilted angles and anti-bird cover occlusion and issues with fast and accurate docking of bolts while the base is moving.

This paper proposes a visual servoing method for the capacitor tower maintenance robot, including bolt pose estimation and visual servoing control. Bolt pose estimation includes four components: constructing a keypoint detection network to identify the approximate position, precise positioning, rapid prediction and calculation of bolt pose. In visual servoing, an improved position-based visual servoing (PBVS) is proposed, which eliminate steady-state error and enhance response speed during dynamic tracking by incorporating integral and differential components.

The bolt detection method exhibits high robustness against varying lighting conditions, partial occlusions, shooting distances and angles. The maximum positioning error at a distance of 250 mm is 2.8 mm. The convergence speed of the improved PBVS is 10% higher than that of the traditional PBVS when the base and target remain relatively stationary. When the base moves at a constant speed, the improved method eliminates steady-state error in dynamic tracking. When the base moves rapidly and intermittently, the maximum error of the improved method in the tracking process is 30% smaller than that of traditional PBVS.

This method enables real-time detection and positioning of bolts in an unstructured environment with tilt angles, variable lighting conditions and occlusion by anti-bird covers. An improved PBVS is proposed to enhance its capability in tracking dynamic targets.

This work aims to develop a web platform for inspecting roof structures for technical assistance supported by drones and artificial intelligence. The tools used were HTML, CSS and JavaScript languages; Firebase software for infrastructure; and Custom Vision for image processing.

This study adopted the design science research approach, and the main stages for the development of the web platform include (1) creation and validation of the roof inspection checklist, (2) validation of the use of Custom Vision as an image recognition tool, and (3) development of the web platform.

The results of automatic recognition showed a percentage of 77.08% accuracy in identifying pathologies in roof images obtained by drones for technical assistance.

This study contributed to developing a drone-integrated roof platform for visual data collection and artificial intelligence for automatic recognition of pathologies, enabling greater efficiency and agility in the collection, processing and analysis of results to guarantee the durability of the building.

This study aims to investigate the influence and impact mechanism of capital tax incentives on firm innovation.

This study employs the difference-in-differences (DID) method, in conjunction with the exogenous impact of accelerated depreciation (AD) pilot policy. This study selects Chinese listed companies from 2010 to 2017 as the research sample.

Firstly, AD exerts a substantial positive effect on the quantity and quality of the innovation output of firms, and the positive impact results primarily from heightened investment in fixed assets, particularly, machinery and equipment. Secondly, the influence of the policy is pronounced in non-state-owned enterprises, mature enterprises, less capital-intensive enterprises and non-high-tech industries, which all exhibit strong innovation incentives. Lastly, the tax incentive policy significantly stimulates firm innovation in the short term, but its long-term impact on innovation incentives lacks statistical significance.

This study highlights the significance of capital tax incentives in facilitating the innovation process in firms.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces.

This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis.

Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively.

To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE.

To the best of the authors’ knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE.

The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages.