Computer-Aided Civil and Infrastructure Engineering最新文献

This study proposes a new method for the rapid and non-contact measurement of cable forces in cable-stayed bridges, including a cable force calculation method based on measured cable shapes and a batch acquisition method for the true shape of cables. First, a nonlinear regression model for estimating cable forces based on measured cable shapes is established, and a Gauss–Newton-based cable force solving method is proposed. Furthermore, terrestrial laser scanning technology is used to collect geometric data of the cables. Meanwhile, automatic segmentation, noise reduction, and centerline extraction algorithms for the cable point cloud are proposed to accurately and efficiently obtain the cable shape. The correctness of the proposed cable force calculation method is verified in a well-designed experiment, with the measurement error of cable forces for 15 test samples being less than 1%. Finally, the proposed point cloud automation processing algorithm and cable force measurement method are fully tested on a cable-stayed bridge with a span of 460 m. The measurement accuracy of the proposed method for actual bridge cable tension is comparable to that of the frequency method, but the detection efficiency on site is nine times higher than that of the traditional frequency method. Overall, this study provides a new measurement method for construction control, health monitoring, intelligent detection, and other fields of cable-stayed bridges.

In order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi-line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.

Fast, accurate damage assessment of numerous buildings for large areas is vital for saving lives, enhancing decision-making, and expediting recovery, thereby increasing urban resilience. The traditional methods, relying on expert mobilization, are slow and unsafe. Recent advances in machine learning (ML) have improved assessments; however, quantum-enhanced ML (QML), a rapidly advancing field, offers greater advantages over classical ML (CML) for large-scale data, enhancing the speed and accuracy of damage assessments. This study explores the viability of leveraging QML to evaluate the safety of reinforced concrete buildings after earthquakes, focusing on classification accuracy only. A QML algorithm is trained using simulation datasets and tested on real-world damaged datasets, with its performance compared to various CML algorithms. The classification results demonstrate the potential of QML to revolutionize seismic damage assessments, offering a promising direction for future research and practical applications.

In recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the “Pseudo-PFLOW” dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo-PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo-PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision-making and analysis.

The cover image is based on the Research Article Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo by Keaton Coletti et al., https://doi.org/10.1111/mice.13123.

The seismic resilience of cities plays a crucial role in achieving the United Nations Sustainability Development Goal. However, despite the occurrence of elevator passenger entrapment in numerous earthquakes, there is a notable lack of studies addressing this sophisticated issue. This study aims to bridge this gap by proposing a novel urban risk assessment model designed to evaluate city-scale earthquake-induced elevator passenger entrapment. The model integrates big data and physics-based approaches. A novel mapping method was developed to estimate city-scale elevator traffic level based on population heatmap data and deep learning. A process-based parallel computing scheme was designed to accelerate the assessment. The applicability was demonstrated based on a real-world urban area comprising 619 buildings. The findings reveal that as the time of the earthquake varies, the risk exhibits significant fluctuations. Additionally, this study highlights that a simplistic correspondence between seismic intensity and passenger entrapment risk can lead to erroneous estimations.

This study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail optimal work zone schedules of realistic-scale road networks in 100 m sections for 365 days, which is beneficial in practice. To this end, an optimization model of the work zone schedule is newly formulated as a mixed-integer programming (MIP) problem, and a novel bilevel solution method for the model utilizing the conventional solver Gurobi Optimizer with MIP algorithms such as root relaxation, dual simplex, barrier methods, and cutting planes is proposed. In application examples, the proposed methodology is applied to two real-world road networks, which confirms that the optimal work zone schedule can be obtained in 313.7 s for a network with 2640, 100 m, sections and in 168,180 s (46 h and 43 min) for a network with 5038, 100 m, sections.

As artificial intelligence technology advances, automated structural design has emerged as a new research focus in recent years. This paper combines finite element method (FEM) and deep reinforcement learning (DRL) to establish a physics-informed framework, named FrameRL, for automated steel frame structure design. FrameRL models the design process of steel frames as a reinforcement learning (RL) process, enabling the agent to simulate a structural engineer's role, interacting with the environment to learn the methods and policies for structural design. Through computer experiments, it is demonstrated that FrameRL can design a safe and economical structure within 1 s, significantly faster than manual design processes. Furthermore, the design performance of FrameRL is compared with traditional optimization algorithms in three typical design cases and a high-rise steel frame case, demonstrating that FrameRL can efficiently complete structural design based on learned design experiences and policies.