Bioinspired Adaptive,Elastic,and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception

Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans.In recent years,extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing.However,there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient,controllable,and scalable way.Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli,an ultrathin,elastic,and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated.The graphene structured thin film is endowed with favourable adaptive and morphable features,which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure.Owing to the introduction of the graphene-based layer,the integrated sensing system can actively detect the water depth with a wide range of 0.3-1.8 m.Furthermore,similar to the fish,the mechanical stimuli from land(e.g.knocking,stomping)and water(e.g.wind blowing,raining,fishing)can also be sensitively captured in real time.This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring,communication,and risk avoidance.

Influence of computation algorithm on the accuracy of rut depth measurement

Rutting is one of the dominant pavement distresses, hence, the accuracy of rut depth measurements can have a substantial impact on the maintenance and rehabilitation （M 8： R） strategies and funding allocation. Different computation algorithms such as straight- edge method and wire line method, which are based on the same raw data, may lead to rut depth estimation which are not always consistent. Therefore, there is an urgent need to assess the impact of algorithm types on the accuracy of rut depth computation. In this paper, a 1B-point-based laser sensor detection technology, commonly accepted in China for rut depth measurements, was used to obtain a database of 85,000 field transverse profiles having three representative rutting shapes with small, medium and high severity rut levels. Based on the reconstruction of real transverse profiles, the consequences from two different algorithms were compared. Results showed that there is a combined effect of rut depth and profile shape on the rut depth computation accuracy. As expected, the dif- ference between the results obtained with the two computation methods increases with deeper rutting sections： when the distress is above 15 mm （severe level）, the average dif- ference between the two computation methods is above 1.5 mm, normally, the wire line method provides larger results. The computation suggests that the rutting shapes have a minimal influence on the results. An in-depth analysis showed that the upheaval outside of the wheel path is a dominant shape factor which results in higher computation differences.