Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC's full potential.

In recent years, there has been an increasing necessity for monitoring facilities like gas or water pipelines to ensure high security and adequate infrastructure maintenance. The pipeline network is very large, and the main problem is its continuous monitoring. In particular, there is the necessity to monitor the cathodic protection (CP) voltage, which ensures maintaining the pipeline under a state of protection from corrosion and avoids considerable damage to the infrastructure. A communication channel is necessary to monitor the pipeline network continuously. Most of the pipeline monitoring systems make use of wireless communication, like global system for mobile communications (GSM) or general packet radio service (GPRS) technology and even Wi-Fi, to transmit the measurements. By their nature, the implementation of these systems is often expensive and furthermore, not all the pipeline is covered by the signals of the mobile operators. In this article, the communication approach is presented, and, in particular, the pipeline is used as a communication channel. Due to the challenges of pipelined transmission, an identification of the characteristic impedance of the medium must be conducted to obtain the best possible performance. This value is used to design a circuit that can match the function generator output to the impedance of the communication channel. The circuit to be made must allow bidirectional communication of the half-duplex type. Given the low frequencies that can be used for communication on the pipe, a low-frequency circulator must be created. Given the frequencies involved, the bidirectional circuit will be composed of operational amplifiers. The presented circulator allows matching the signal generator output impedance with the pipeline input impedance, to obtain an improvement in the transmission distance achievable using the pipeline as a communication channel.