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Data collection of the pavement condition is the main core of the pavement management systems. The automated data collection method has some advantages over the traditional one such as safety, consistency, speed, and repeatability. However, one of the main disadvantages of the automated data collection is the high cost of required devices. It is necessary to present an innovative cost-effective automated data collection system with adequate accuracy and precision. Pavement roughness is one of the most important parameters showing the pavement condition and directly affecting the ride quality. The main aim of this research is to develop a new automated cost-effective data collection device for measuring roughness. An extra wheel equipped with accelerometers and a geographical positioning system are utilized for evaluating pavement roughness. Longitudinal profiles are employed to calculate the International Roughness Index through the application of a linear model with a high coefficient of determination of 0.87. The root-mean-square error and the average error percentage are sufficiently low, equal 10% and 20%, respectively.

Road surface friction is one of the most important parameters determining road safety and macrotexture is one of the important factors affecting it. There are two types of traditional and automated data gathering methods. One of the disadvantages of automated methods is the high cost of purchasing or manufacturing. The purpose of this study is evaluating the feasibility of a low-cost dynamic method based on tire/road noise to evaluate the macrotexture of cracked non-porous asphalt pavements. To this end, the tire/road noise gathering system has been developed inspired by the ISO 11819-2 standard. The developed system has been used for data collection alongside with the health monitoring system of the Amirkabir University of Technology. After data gathering, the relationship between macrotexture indices (laser-based) and tire/road noise features has been evaluated. Using artificial neural networks, models for relating different sound and macrotexture indices in each frequency band are presented with the best correlation result being R2 = 0.76 between MPD and power spectral density in the 500 to 1500 Hz frequency band. The results indicate that in cracked pavement surfaces, by focusing on the vibration mechanism, acceptable results can be achieved in macrotexture monitoring by using interaction noise.

The focus of this article is to define goals and resulting action plans that can be collectively embraced by interested stakeholders to facilitate new therapeutic approaches to mitigate chronic kidney disease progression. The specific goals include identifying druggable targets, increasing the capacity for preclinical and early clinical development, broadening the availability of new therapeutic approaches, and increasing investment in the development of new therapies to limit chronic kidney disease. Key deliverables include the establishment of new regional, national, and global consortia; development of clinical trial networks; and creation of programs to support the temporary mutual movement of scientists between academia and the biotechnology and pharmaceutical sector. Other deliverables include cataloging and maintaining up-to-date records to collate progress in renal research and development, inventorying the capacity of research and clinical networks, and describing methods to ensure novel drug development.

In this study, an acoustical-based macrotexture measurement method that uses the friction-induced mechanism of tire/road noise has been proposed. This mechanism influences the frequencies above 1 KHz and is the dominant mechanism for frequencies above 3 KHz. The airflow due to this mechanism passes through the macrotexture which can be represented using convolution in the time-domain of the airflow, which is viewed as the input signal, and macrotexture, which is considered as the transmission channel. Using the Cepstral processing method, the related features to macrotexture airflow can be extracted and the macrotexture can be evaluated. To investigate the proposed method, interaction noise was measured for six uniform non-porous asphaltic test tracks with different macrotextures. The tire/road noise signal is passed through a bandpass filter (3–5 KHz). By liftering the cepstrum components in the range of 2–50, the feature vector has been obtained and then used as input to a multiclass SVM, resulting in a precision error of 4%.

Data collection and the associated automated methods are one of the main building blocks of pavement management systems. One of the main problems associated with automated data collection methods is the high cost of the equipment involved. The aim of this research is to develop a cost-effective system for dense-graded asphalt pavement macrotexture monitoring. To this end, a system based on the tire/road noise, utilizing microphones and employing cepstral signal processing has been developed. The proposed method is compared with the current state of the art PCA based method and is shown that the precision error of the proposed method is about 7%, which outperforms the previous state of the art. To enhance the precision of the cepstral method for the vehicle speed variation amongst the collected dataset, the combination of the cepstral signal processing with Gaussian mixture models is proposed which results in the final precision error of 8%.