The in-situ dynamic modulus property of asphalt mixtures is critical to aiding the decision-making of pavement maintenance and rehabilitation. With the recent advancement in data science and sensing technologies, embedded sensors have been applied to collect the in-situ signal induced by traffic loading and estimate the traffic information. However, very limited studies have focused on evaluating the mechanical properties of pavement materials and structures using embedded sensors. This paper aims to present a practical approach to performing the in-situ dynamic modulus test and develop an in-situ dynamic modulus predictive model using Artificial Neural Network (ANN) based on real-time sensing data. Particle-size wireless sensors were implemented in several pavement sections to collect data under vehicular loading. An empirical mode decomposition (EMD) method was introduced to calculate the intrinsic modes of the collected data as the ANN model inputs. Laboratory dynamic modulus tests using the same material as the paving projects were also performed with embedded wireless sensors. Those laboratory data, combined with the field sensing data, were used as the training and testing dataset for developing the ANN model. The results show that the developed ANN model, when adequately trained with particle-level sensing data, is feasible and robust for predicting the in-situ dynamic modulus of asphalt pavement.

Compaction is a critical step in asphalt roadway construction to determine the pavement’s quality and service life. Current field compactions are mainly based on test strips and engineers’ experiences. Such empirical-based approaches sometimes cause compaction problems especially when new materials are implemented. Intelligent compaction (IC) technology was invented to improve compaction quality. The real-time collection and visualization of the pavement responses and temperature can greatly improve compaction uniformity. However, the material viscoelastic property and the complex pavement structure still hinder the establishment of the correlation between the Intelligent Compaction Measurement Value (ICMV) and the compactability of asphalt pavement. This paper aims to establish compaction prediction models based on machine learning (ML) algorithms and sensing technology. Given the strong correlation between the mixture’s compactability and the particle compaction characteristics, a particle-size wireless sensor, SmartRock, was used to collect the particle kinematic behaviors during compaction. 11 asphalt mixtures were compacted with an embedded SmartRock sensor. Three ML models and three dataset scenarios were applied to predict the compaction condition, and a higher than 98% prediction accuracy was achieved. The results revealed that combining the ML approach and sensing data is appropriate and practical for predicting the compaction condition of the asphalt mixture. Future studies shall further evaluate the algorithm based on field compaction data so that intelligent sensing technology can be implemented to improve compaction quality.

The advanced non-destructive tests (NDT) have become increasingly popular in the structural performance evaluation of transportation infrastructures including pavements. Among them, Falling Weight Deflectometer (FWD) and Spectral Analysis of Surface Waves (SASW) are the two most widely used methods in modulus measurement. Although popularly used, FWD methods base the analysis purely on the deflection characteristics of the pavement and the back-calculated modulus values are usually not unique for different data processing methods. SASW is based on fundamental spectral analysis of the Rayleigh waves but is practically limited in field operation due to its efficiency concern. On the other hand, the impact loading applied by the FWD will generate the Rayleigh wave, and the geophones used in FWD can capture Rayleigh wave fluctuation and measure the particle motion for phase velocity calculations. It is theoretically possible to use the deflection-time history collected by FWD to perform dispersion analysis based on the Rayleigh wave propagation theory, as used in the SASW method. Inspired from that, the main objective of this paper is to propose a new approach of using the deflection-time history collected by FWD to develop the dispersion curves and thus obtain the in-situ modulus of the layered asphalt pavement. This approach is referred as the FWD dispersion curve method. Two in-service semirigid base asphalt pavement segments were used as case studies to explain the new methodology. The results of layered modulus from the FWD dispersion curve method were compared with the traditional FWD and SASW method, as well as the laboratory Indirect Tensile (IDT) testing results using field cores. Findings demonstrated the feasibility of the FWD dispersion curve method in accurately capturing the layered modulus profile of the asphalt pavements.

Coupling the endface of an optical fiber to an integrated optical component is currently a low-throughput and costly manual process in the fabrication of the optical devices. In order to meet the high-volume demand for commercial optoelectronic devices, coupling must be automated. This paper presents a robotic positioning system and corresponding path planning strategy based on both the position and light intensity feedback. In this work, a micro-robotic positioning system with 3 degrees of freedoms (DOFs) is developed and integrated with an optical microscopy. Then the fuzzy controller is developed to design the trajectory. Lastly, simulation and experimental results demonstrate the accuracy and efficiency of the proposed system. Compared with the traditional manual method, the robotic positioning system can realize the coupling within 40 seconds. This method will have a significant impact on the automatic process of the micro manufacture field.

Accurate alignment of the watch hand is a critical procedure in the industrial production of watches. However, compared with the traditional ideal laboratorial case, the watch-hand alignment suffers various external disturbances from the aged equipment and industrial operating conditions, which seriously influence the alignment precision. To achieve accurate watchhand alignment in the complex industrial environment, this paper develops a robotic micromanipulation system and proposes a corresponding robust control strategy. First, a micromanipulation system with five degrees of freedom is set up and integrated with an optical microscope. Then, a set of proper machine vision methods is adopted to obtain accurate position information in disregard of the interference terms. Third, the dynamic model of the controlled watch hand is built with consideration of the external disturbances from the production environment. Since the velocity information cannot be observed accurately through the machine vision process, we design a state observer to acquire the position and velocity values for the following control strategy. After that, a sliding mode controller with the radical basis function neural network is proposed to achieve high-precision alignment while resisting the external disturbance. Finally, simulation and experimental results prove that the proposed method can realize the alignment of the watch hand within 6 s with the accuracy of 2 × 10-3rad, which is enhanced at least two times compared with the traditional manual method.