Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle's number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results.

中文翻译：

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基于导电胶凝材料的智能路面动态称量算法。

通过将道路转变为能够自我感应的多功能元件，智能材料是降低连续监测道路基础设施所需仪器成本的有前途的技术。这项研究研究了一种新的算法，该算法通过动态称重（WIM）表征功能为智能路面提供支持。感兴趣的应用领域是安装在单独部分上的桥梁上的基于水泥的智能路面。每个部分都将由车辆通过引起的轴向应变转换为由智能材料的压阻效应引起的可测量的电阻变化。WIM表征算法如下。首先，来自轴的基础信号是从配备有智能路面的结构的有限元模型生成的，并且要承受给定的车辆载荷。其次，通过找到适当的基本信号的数量和权重来匹配测量的信号，该数量和权重将使数字信号和测量的信号之间的误差最小化，从而获得有关车辆轴数和每轴重量的信息，从而实现车辆分类能力。第三，在智能人行道上比较测量信号的时间相关性，以确定车辆的重量。使用Eurocode定义的三种类型的卡车对所提出的算法进行了数值验证。结果证明了该算法在进行WIM表征时的能力，即使当两辆不同的卡车在同一人行横道上以不同方向行驶时也是如此。然后，进行噪音研究，