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Among large-span bridges, arch bridges have relatively high stiffness, which may lead to large dynamic amplification factors (DAFs). The DAFs suggested by current codes mostly originate from common simply supported beam bridges. Previous DAF studies on dynamic vehicle–bridge solutions for arch bridges have mainly focused on one or two side-by-side vehicles. However, complex traffic flows with randomness rather than one or two vehicles act on bridges. DAFs that consider the effects of random traffic flows have not previously been reported. In this study, a random traffic–bridge vibration solution was established to explore the DAFs of arch bridge components. The randomness of the traffic parameters and road roughness was explored. The randomness of the external excitation, including traffic flow and road roughness, resulted in random dynamic responses and DAFs of the arch bridge components. Normal distributions could be used to fit the DAF distributions of each arch bridge component under random vehicle spacing, weight, speed, and road roughness. Under random traffic parameters, the coefficients of variation of the DAFs exceeded the 5% accepted level. The shortest suspenders were more sensitive to the randomness of the traffic flow parameters. Both the mean and coefficient of variation of the DAFs increased with worsening of the road conditions. The influence of the randomness of the road roughness on the DAFs of arch bridges must be considered, particularly for the shortest suspender. The 95% upper confidence limits of the DAFs for all components may be greater than the suggested values in the code. [ABSTRACT FROM AUTHOR]

Cracking is one of the most common bridge diseases. If bridge cracks are not repaired: in time, they may cause gradual changes to the concrete structure, which can seriously affect its strength. A network called YOLOv5-TS is what we suggest to detect intelligently bridge surface cracks in images. To improve the network performance, we integrate SPPCSPC into the original YOLOv5 network to ensure adaptive image output and obtain receptive fields of various sizes. Meanwhile, transposed convolution is incorporated to improve the capacity of the network for learning weights on its own and reduce characteristic information loss. In response to the diverse morphology of bridge cracks, cracks are identified according to their mechanical causes crack inclination, and divided into four categories: horizontal cracks (0 ∘ –20 ∘ ), low-angle cracks (20 ∘ –45 ∘ ), vertical cracks (70 ∘ –90 ∘ ) and high-angle cracks (45 ∘ –70 ∘ ). Experiments on the ZJU SYG crack data set confirm that the proposed YOLOv5-TS has a better crack intelligent identification effect on bridge surface images than other compared baselines. The best performance of YOLOv5-TS is found in mAP@0.5 (0.752), mAP@0.5:0.95 (0.518), and recall (0.794), thus demonstrating the model's practical value. [ABSTRACT FROM AUTHOR]

The effect of the strong electron acceptor, 2,3,5,6-tetrafluoro-7,7,8,8- tetracyanoquinodimethane (F4-TCNQ), on poly(3-hexylthiophene) (P3HT) aggregates is studied. F4-TCNQ is commonly used as a dopant for P3HT, however, relatively little is currently known about its effect on polymer conformation and packing in the presence of fullerenes. Resonance Raman and optical spectra of pristine P3HT or blends with [6,6]-phenyl-C 61-butyric acid methyl ester (PCBM) doped with F4-TCNQ up to ∼10% w/w show a loss of pristine-type P3HT aggregates with increasing dopant concentration. Complexed P3HT chains possess greater backbone planarity due to hole injection which is corroborated from density functional theory (DFT) calculations of oligothiophene surrogates and F4-TCNQ. Morphologies of doped P3HT/PCBM systems are characterized using scanning transmission electron microscopy (STEM) with electron energy loss spectroscopy (EELS) detection and images reveal mixed clusters of P3HT/F4-TCNQ fibril-like domains that increase in size with dopant loading. The apparent preference of F4-TCNQ for P3HT aggregates is attributed to efficient charge separation stemming owing to the more polarizable nature of chains comprising the aggregate π-stack. © 2013 The Royal Society of Chemistry.

Vortex-induced vibrations (VIVs) have been frequently observed on long-span bridges (LSBs) in recent years. Unlike other destructive aerodynamic phenomena of LSBs, VIVs are self-limited in amplitude, primarily affecting the serviceability of LSBs through unpleasant users' feelings characterized by human body vibration. Most existing studies discussed this issue based on a popular human body vibration measure, the human comfort index (HCI) in ISO 2631-1. However, the HCI is primarily concerned with vibration above 0.5 Hz, which might be unsuitable for disclosing the influence of VIV because of the low-frequency features of LSBs' VIVs. To address this limitation, this study advocates using the motion sickness index (MSI) to revisit the serviceability of LSBs experiencing VIVs based on an innovative wind-traffic-bridge simulation platform. Different from current studies exclusively focusing on vehicle riders, this paper additionally incorporates a vibration model for standing persons to understand the feelings of the pedestrians on the bridge. On this basis, the influence of VIV and traffic load is comprehensively examined. The results indicate that the HCI is inappropriate for exploring the serviceability of LSBs under VIVs regarding users' feelings, but the MSI is a good alternative. Moreover, the increasing traffic load can obviously mitigate the adverse effect of VIVs on the bridge's serviceability, which may be utilized to control VIVs of LSBs in real-world engineering practice. [ABSTRACT FROM AUTHOR]