Ultra-high performance fiber-reinforced concrete (UHPFRC) has received extensive attention from scholars and engineers due to its excellent mechanical properties and durability. However, there is a mutually restrictive relationship between the workability and mechanical properties of UHPFRC. Specifically, the addition of fibers will affect the workability of fresh UHPFRC, and the workability of fresh UHPFRC will also affect the dispersion and arrangement of fibers, thus significantly influencing the mechanical properties of hardened UHPFRC. This paper first analyzes the research status of UHPFRC and the relationship between its workability and mechanical properties. Subsequently, it outlines the test methods and indicators of UHPFRC workability, including fluidity, slump, V-funnel passing time, and rheology. Then, it reviews the impacts of metal fibers, synthetic fibers, hybrid fibers, and other fibers on the workability and mechanical properties of UHPFRC, and presents a reasonable range of fiber dosage for workability and mechanical properties. Key findings include: (1) Steel fibers within 1%–2% volume optimize workability-mechanical balance, while exceeding 2.5% reduces compressive strength by 7%–30%; (2) Hybrid steel-polypropylene fibers enhance toughness by 65%; (3) Fiber orientation control via rheology-modifying admixtures improves flexural strength by up to 64%. This review establishes a fiber factor (V⋅L/D) for predictive mix design, advancing beyond empirical approaches in prior studies.

Carbon nanotube-reinforced cement composites have gained significant attention due to their enhanced mechanical properties, particularly in compressive and flexural strength. Despite extensive research, the influence of various parameters on these properties remains inadequately understood, primarily due to the complex interactions within the composites. This study addresses this gap by employing machine learning techniques to conduct a sensitivity analysis on the compressive and flexural strength of carbon nanotube-reinforced cement composites. It systematically evaluates nine data-preprocessing techniques and benchmarks eleven machine-learning algorithms to reveal trade-offs between predictive accuracy and computational complexity, which has not previously been explored in carbon nanotube-reinforced cement composite research. In this regard, four main factors are considered in the sensitivity analysis, which are the machine learning model type, the data pre-processing technique, and the effect of the concrete constituent materials on the compressive and flexural strength both globally through feature importance assessment and locally through partial dependence analysis. Accordingly, this research optimizes ninety-nine models representing combinations of eleven machine learning algorithms and nine data preprocessing techniques to accurately predict the mechanical properties of carbon nanotube-reinforced cement composites. Moreover, the study aims to unravel the relationships between different parameters and their impact on the composite’s strength by utilizing feature importance and partial dependence analyses. This research is crucial as it provides a comprehensive understanding of the factors influencing the performance of carbon nanotube-reinforced cement composites, which is vital for their efficient design and application in construction. The use of machine learning in this context not only enhances predictive accuracy but also offers insights that are often challenging to obtain through traditional experimental methods. The findings contribute to the field by highlighting the potential of advanced data-driven approaches in optimizing and understanding advanced composite materials, paving the way for more durable and resilient construction materials.

The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the practical engineering applications. To further improve the applicability of ANN in concrete, the model can consider the combination of multiple algorithms and the expansion of data samples. The review can provide new research ideas for development of concrete performance prediction.

To investigate the performance of utilizing the shape memory effect of SMA (Shape Memory Alloy) wire to generate recovery stress, this paper performed single heating recovery stress tests and reciprocating heating-cooling rec... | Find, read and cite all the research you need on Tech Science Press

To mitigate the severe abrasion damage caused by high-velocity water flow in hydraulic engineering applications in Xizang, China, this study systematically optimized key mix design parameters, including aggregate gradation, sand ratio, fly ash content, and superplasticizer dosage. Based on the optimized mix, the combined effects of an abrasion-resistance enhancement admixture (AEA) and silica fume (SF) on the abrasion resistance of self-compacting concrete (SCC) were examined. The results demonstrated that the appropriate incorporation of AEA and SF significantly improved the abrasion resistance of SCC without compromising its workability. The proposed mix design not only achieves superior abrasion resistance but also provides practical guidance for the material design and engineering application of durable hydraulic concrete in harsh environments. Future research will focus on comprehensive durability assessments by simulating extreme hydraulic conditions, including sustained exposure to high-velocity sediment-laden flows, repeated freeze-thaw cycles, and corrosive salt spray environments, to thoroughly evaluate the long-term performance evolution of abrasion-resistant self-compacting concrete. Meanwhile, advanced microstructural analytical methods should be applied toelaborate the synergistic mechanisms of abrasion-resistance enhancement admixture (AEA), silica fume (SF), and steel fibers in altering the hydration product formation, optimizing the distribution of pore structure, and strengthening interfacial transition zones, to establish a solid scientific foundation for the development of high-performance composite materials.

Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis.However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance.To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper.RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention.Combined with transfer learning techniques, the transfer RST model uses pre-trained parameters from ImageNet.A new end-to-end method for fault diagnosis based on deep transfer RST is proposed.Firstly, wavelet transform transforms the vibration signal into a wavelet time-frequency diagram.The signal's time-frequency domain representation can be represented simultaneously.Secondly, the wavelet time-frequency diagram is the input of the RST model to obtain the fault type.Finally, our method is verified on public and self-built datasets.Experimental results show the superior performance of our method by comparing it with a shallow neural network.

With the advent of the big data era and the rise of Industrial Revolution 4.0, digital twins (DTs) have gained significant attention in various industries. DTs offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering industry. In this paper, 605 documents obtained from the search were first analysed using CiteSpace for literature visualisation, and an author co-occurrence network, a keyword co-occurrence network, and a keyword clustering set were obtained. Next, through a literature review of 86 papers, this paper summarises the current status of DT application in civil engineering based on a review of the origins, concepts, and implementation techniques of DTs, and it introduces the application of DTs in the full project lifecycle. This study shows that DTs have great potential to address many of the challenges faced by civil engineering. In this regard, the paper also presents some thoughts on the future directions of DT research.

This study provides a comprehensive analysis of collision and impact problems' numerical solutions, focusing on geometric, contact, and material nonlinearities, all essential in solving large deformation problems during a collision. The initial discussion revolves around the stress and strain of large deformation during a collision, followed by explanations of the fundamental finite element solution method for addressing such issues. The hourglass mode's control methods, such as single-point reduced integration and contact-collision algorithms are detailed and implemented within the finite element framework. The paper further investigates the dynamic response and failure modes of Reinforced Concrete (RC) members under asymmetrical impact using a 3D discrete model in ABAQUS that treats steel bars and concrete connections as bond slips. The model's validity was confirmed through comparisons with the node-sharing algorithm and system energy relations. Experimental parameters were varied, including the rigid hammer's mass and initial velocity, concrete strength, and longitudinal and stirrup reinforcement ratios. Findings indicated that increased hammer mass and velocity escalated RC member damage, while increased reinforcement ratios improved impact resistance. Contrarily, increased concrete strength did not significantly reduce lateral displacement when considering strain rate effects. The study also explores material nonlinearity, examining different materials' responses to collision-induced forces and stresses, demonstrated through an elastic rod impact case study. The paper proposes a damage criterion based on the residual axial load-bearing capacity for assessing damage under the asymmetrical impact, showing a correlation between damage degree hammer mass and initial velocity. The results, validated through comparison with theoretical and analytical solutions, verify the ABAQUS program's accuracy and reliability in analyzing impact problems, offering valuable insights into collision and impact problems' nonlinearities and practical strategies for enhancing RC structures' resilience under dynamic stress.

Automatic control technology is the basis of road robot improvement, according to the characteristics of construction equipment and functions, the research will be input type perception from positioning acquisition, real-world monitoring, the process will use RTK-GNSS positional perception technology, by projecting the left side of the earth from Gauss-Krueger projection method, and then carry out the Cartesian conversion based on the characteristics of drawing; steering control system is the core of the electric drive unmanned module, on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles, the steering system key components such as direction, torque sensor, drive motor and other models are established, the joint simulation model of unmanned engineering vehicles is established, the steering controller is designed using the PID method, the simulation results show that the control method can meet the construction path demand for automatic steering. The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm, and never realize the construction-based path planning, and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm. With the collaboration of various modules, the automatic construction simulation results of this robot show that the design path and control method is effective.

As an important component of load transfer, various fatigue damages occur in the track as the rail service life and train traffic increase gradually, such as rail corrugation, rail joint damage, uneven thermite welds, rail sq... | Find, read and cite all the research you need on Tech Science Press