Search Results (9)

The integration of 3D printing with smart infrastructure presents a transformative opportunity in urban planning, construction, and engineering, enhancing efficiency, flexibility, and sustainability. By leveraging additive manufacturing alongside digitalization, artificial intelligence (AI), and the Internet of Things (IoT), this technology enables the creation of customized, lightweight, and sensor-embedded structures. This work analyzes both the advantages and challenges of applying 3D printing in smart infrastructure, focusing on material optimization, rapid prototyping, and automated fabrication, which significantly reduce construction time, labor costs, and material waste. Applications such as 3D-printed bridges, modular housing, and IoT-integrated urban furniture exhibit its potential in contributing towards resilient and resource-efficient cities. However, despite these benefits, significant challenges hinder large-scale adoption. Issues of scalability, particularly in the fabrication of large and load-bearing structures, remain unresolved, requiring advancements in high-speed printing techniques, material reinforcement strategies, and hybrid construction methods. Furthermore, regulatory uncertainties and the absence of standardized guidelines create barriers to implementation. The lack of comprehensive building codes, certification protocols, and quality assurance measures for 3D-printed structures limits their widespread acceptance in mainstream construction. Overcoming these limitations necessitates research into AI-driven process optimization, multi-material printing, and international standardization efforts. By assisting towards overcoming these challenges, 3D printing has the potential to redefine urban development, making infrastructure more adaptive, cost-effective, and environmentally sustainable. This work provides a critical evaluation of the current capabilities and limitations of 3D printing in smart infrastructure towards achieving full-scale implementation and regulatory compliance. Full article

With the continuous increase in bridge lifespans, the rapid check and evaluation of the vertical bearing capacity for the pile foundations of existing bridges have been in greater demand. The usual practice is to carry out compression bearing tests under static loads in order to obtain the accurate ratio of the dynamic to static stiffness. However, it is difficult and costly to conduct in situ experiments for each pile foundation. Herein, a rapid evaluation method to measure the vertical bearing capacity of bridge pile foundations is proposed. Firstly, a 3D-bearing cap–pile group–soil interaction model was established to simulate a bearing test of a pile foundation that was subject to static loads and dynamic loads, and then the numerical results were validated by in situ dynamic and static loading tests on an abandoned bridge pier with the same pile group foundation; the dataset for machine learning was constructed using the numerical results, and finally, the bearing capacity of the pile foundation could be predicted rapidly. The results show the following outcomes: the established numerical model can effectively simulate dynamic and static loading tests of pile foundations; the intelligent prediction model based on machine learning can predict the ratio of static stiffness to dynamic stiffness and can thus rapidly evaluate the vertical residual bearing capacity and the designed ultimate loading capacity, allowing for the nondestructive testing and evaluation of the pile foundations of existing bridges. Full article

Single-pier, dual-bearing bridges are susceptible to effects such as concrete creep, thermal expansion, and uneven foundation settlement. When combined with eccentric loading from heavy vehicles, these factors collectively can significantly increase the risk of bridge overturning. To address this risk, a comprehensive analysis of the bridge overturning mechanism was conducted. Considering the current limitations of health monitoring in bearing reaction force (BRF) measurement and risk mitigation, an adjustable intelligent bearing based on pressure sensing and self-locking principles was developed. Its mechanical performance was analyzed under the most unfavorable load conditions. To further validate the approach, a specific experimental bridge was used as a case study. The effectiveness of the force measurement and height adjustment functions was evaluated through moving load experiments. The results showed that the force measurement function accurately captured dynamic BRF changes within a precision range of ±0.1% FS and demonstrated high sensitivity to instantaneous impact effects. The height adjustment function achieved a reaction force change of up to 40 kN within the maximum adjustment range of 1.2 mm, significantly improving the load distribution of the bridge. These findings validated the reliability of the proposed intelligent bearing in real-time monitoring and proactive risk adjustment. This effectively overcomes the limitations of existing bearings, which only perform passive monitoring. Overall, it achieves the real-time monitoring of BRF and proactive control of bridge overturning risks. Full article

In the realm of urban geotechnical infrastructure development, accurate estimation of the California Bearing Ratio (CBR), a key indicator of the strength of unbound granular material and subgrade soil, is paramount for pavement design. Traditional laboratory methods for obtaining CBR values are time-consuming and labor-intensive, prompting the exploration of novel computational strategies. This paper illustrates the development and application of machine learning techniques—multivariate linear regression (MLR), artificial neural networks (ANN), and the adaptive neuro-fuzzy inference system (ANFIS)—to indirectly predict the CBR based on the soil type, plasticity index (PI), and maximum dry density (MDD). Our study analyzed 2191 soil samples for parameters including PI, MDD, particle size distribution, and CBR, leveraging theoretical calculations and big data analysis. The ANFIS demonstrated superior performance in CBR prediction with an R² value of 0.81, surpassing both MLR and ANN. Sensitivity analysis revealed the PI as the most significant parameter affecting the CBR, carrying a relative importance of 46%. The findings underscore the potent potential of machine learning and neuro-fuzzy inference systems in the sustainable management of non-renewable urban resources and provide crucial insights for urban planning, construction materials selection, and infrastructure development. This study bridges the gap between computational techniques and geotechnical engineering, heralding a new era of intelligent urban resource management. Full article

As an important component connecting the upper and lower structures of a bridge, bridge bearings can reliably transfer vertical and horizontal loads to a foundation. Bearing capacity needs to be monitored during construction and maintenance. To create an intelligent pot bearing, a portable small spot welding machine is used to weld pipe-type welding strain gauges to the pot bearing to measure strain and force values. The research contents of this paper include the finite element analysis of a basin bearing, optimal arrangement of welding strain gauges, calibration testing, and temperature compensation testing of the intelligent basin bearing of the welding strain gauges. Polynomial fitting is used for the fitting and analysis of test data. The results indicate that the developed intelligent pot bearing has a high-precision force measurement function and that after temperature compensation, the measurement error is within 1.8%. The intelligent pot bearing has a low production cost, and the pipe-type welding strain gauges can be conveniently replaced. The novelty is that the bearing adopts a robust pipe-type welding strain gauge and that automatic temperature compensation is used. Therefore, the research results have excellent engineering application value. Full article

Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage. Full article

In recent advances, deep learning-based methods have been broadly applied in fault diagnosis, while most existing studies assume that source domain and target domain data follow the same distribution. As differences in operating conditions lead to the deterioration of diagnosis performance, domain adaptation technology has been introduced to bridge the distribution gap. However, most existing approaches generally assume that source domain labels are available under all health conditions during training, which is incompatible with the actual industrial situation. To this end, this paper proposes a semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Firstly, the Gramian Angular Field method is introduced to convert time domain vibration signals into images. Secondly, a semi-supervised learning-based label generating module is designed to generate artificial labels for unlabeled images. Finally, the dynamic adversarial transfer network is proposed to extract the domain-invariant features of all signal images and provide reliable diagnosis results. Two case studies were conducted on public rolling bearing datasets to evaluate the diagnostic performance. An experiment under variable operating conditions and an experiment with different numbers of source domain labels were carried out to verify the generalization and robustness of the proposed approach, respectively. Experiment results demonstrate that the proposed method can achieve high diagnosis accuracy when dealing with cross-domain tasks with deficient source domain labels, which may be more feasible in engineering applications than conventional methodologies. Full article

Lost circulation in fractured formation is the first major technical problem that restricts improvements in the quality and efficiency of oil and gas drilling engineering. Improving the success rate of one-time lost circulation control is an urgent demand to ensure “safe, efficient and economic” drilling in oilfields all over the world. In view of the current situation, where drilling fluid loss occurs and the plugging mechanism of fractured formation is not perfect, this paper systematically summarizes the drilling fluid loss mechanism and model of fractured formation. The mechanism and the main influencing factors to improve the formation’s pressure-bearing capacity, based on stress cage theory, fracture closure stress theory, fracture extension stress theory and chemical strengthening wellbore theory, are analyzed in detail. The properties and interaction mechanism of various types of lost circulation materials, such as bridging, high water loss, curable, liquid absorption and expansion and flexible gel, are introduced. The characteristics and distribution of drilling fluid loss in fractured formation are also clarified. Furthermore, it is proposed that lost circulation control technology for fractured formation should focus on the development of big data and intelligence, and adaptive and efficient intelligent lost circulation material should be continuously developed, which lays a theoretical foundation for improving the success rate of lost circulation control in fractured formation. Full article

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent. Full article