Gravel road condition assessment is crucial for effective infrastructure maintenance. Visual windshield surveys, the traditional assessment method, are often inefficient and subjective, leading to inconsistencies in maintenance decisions. This paper presents the preliminary results of a study that tests different methods for assessing the condition of gravel roads and explores the possibility of integrating these assessment methods for improved effectiveness. Field experiments were conducted on a gravel road directly after maintenance and several months later, after corrugations had formed. Visual assessments and the International Roughness Index (IRI) measurements were performed using the Roadroid smartphone application. At the same time, the vehicle vibration response data was collected using an Integrated Electronics Piezo-Electric (IEPE) accelerometer. The findings demonstrate a correlation between higher IRI values, increased vehicle vibration levels and the presence of corrugations. Analysing vehicle vibration responses in different frequency sub-bands also provided insights into specific road defects, such as loose gravel and corrugations. Integrating these data sources provides a more accurate and detailed insight into the gravel road conditions that would enable targeted maintenance interventions. There is potential to incorporate artificial intelligence (AI) for automated road condition assessment and predictive maintenance, for instance, through a machine learning model. The model trained on the collected data would then assess the gravel road condition and predict future deterioration, thus optimising maintenance resource allocation and improving efficiency and cost-effectiveness in managing gravel roads.

This paper examines a dynamic sampling technique for optimising the collection and storage of gravel road condition data by reducing redundancy while preserving accuracy. Advances in sensor technology and Information and Communication Technology (ICT) enable large-scale condition assessment but generate vast amounts of data, making efficient collection and storage essential. The Dynamic Sampling Rate Algorithm-Parametric Machine Learning Optimisation (DSRA-PMLO) was applied to simulated datasets and vehicle vibration response (VVR) signals collected using an Integrated Electronics Piezo-Electric (IEPE) accelerometer on three gravel roads with varying surface conditions. The algorithm was evaluated using error thresholds of 5-50%. Results show that DSRA-PMLO substantially reduces data volume without significant information loss. At a 20% error threshold, approximately 50% of the original data was sufficient for accurate condition assessment, while faults including potholes, corrugation, and loose gravel remained reliably detectable. Reduced data collection also lowers storage, bandwidth, and energy requirements.

Manufacturing digitalization and automation research has expanded rapidly over the past decades. However, the current literature often presents fragmented views, lacking a comprehensive understanding of its evolution. Due to the vast number of publications, traditional literature reviews are impractical for broad fields like manufacturing digitalization and automation. This study leverages recent advancements in Artificial Intelligence (AI), Large Language Models (LLMs), and Natural Language Processing (NLP) to analyze 31,914 scientific papers from 1970 to 2023, providing a knowledge structure of the field. Moreover, we provide a roadmap of the field's evolution using Dynamic Topic Modeling (DTM). We note emerging trends in energy efficiency (since 2004), composite materials (2006), cybersecurity (2008), robotics (2014), and AI (2016), while simulation, scheduling, and process planning maintain steady and consistent research interests. Additionally, we observe a decay in certain research areas such as manufacturing automation protocol, a once-prominent area in the 1980s introduced by General Motors. We introduce a technology portfolio assessment framework categorizing technologies into Emerging, Established, Experimental, and Decaying quadrants. The proposed framework is based on the findings of this study, technology life cycle, creative destruction, and technological paradigms theories. The findings also offer insights into thematic shifts across the literature, consistent with creative destruction and technological paradigm theories. These findings hold implications for academia, industry, and policymakers, supporting more strategic innovation and resource allocation in the manufacturing sector.

Assessing road conditions is crucial for effective infrastructure maintenance, particularly on gravel roads, which can deteriorate rapidly and unpredictably. This study explores the use of vehicle vibration response to classify gravel road conditions into three categories (Good Road, Fairly Good Road and Bad Road). The vehicle vibration response signals were collected using an Integrated Electronics PiezoElectric (IEPE) accelerometer mounted on the vehicle’s dashboard while driving across gravel roads with varying conditions. The acquired signals were processed by first dividing the vehicle vibration response signal into eight adjacent frequency sub-bands using a Finite Impulse Response (FIR) filter as a band bass filter. Estimates of the statistical properties, including variance, root mean square (RMS), and the power spectral density of the vehicle vibration response for each test road and its respective frequency sub-bands, were compared to distinguish the condition of the gravel roads. The findings were then validated with the Swedish classification standard, TDOK 2014:0135, as a benchmark for developing a condition classification framework. This will establish a widely adopted objective approach to enable reliable, automated condition assessment and classification for planning maintenance scheduling and improved resource allocation. The study provides a statistical, sensor-based method for monitoring and classifying gravel road surface conditions. 

This paper presents the status of an ongoing comprehensive process innovation—Welded Structures 4 Tomorrow (W4T)—implemented at Volvo Construction Equipment (Volvo CE) to address critical limitations in the fatigue design and manufacturing of welded structures. It introduces a new fatigue assessment approach, the Structural Fracture Mechanics (SFM) Method, based on fracture mechanics principles and, in certain cases, extended to include crack initiation modelling. This paper focuses exclusively on weld roots, with weld toes addressed separately in an on-going research program. The SFM approach enables more reliable fatigue life predictions for complex weld geometries while reducing design and simulation effort. The approach is integrated into a broader workflow that includes digitized vehicle load simulations, streamlined Computer-Aided-Design (CAD) to Finite Element Method (FEM) modelling, and modernized manufacturing processes, including robotic dressing for Tungsten Inert Gas (TIG) welding and 3D laser cutting. The SFM method has been implemented in Volvo CE’s development process, supported by a dedicated toolchain developed in-house for preprocessing, calculation, and postprocessing of fatigue life predictions, and is validated through an extensive test program focused on crack propagation in welds, as well as real-world industrial use in Volvo CE. This integrated approach contributes to shorter development cycles, improved production efficiency, and enhanced structural robustness—aligning with both performance and sustainability goals.

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA); however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change.

Gravel road deterioration leads to the disintegration of gravel, resulting in road irregularities such as potholes, rutting and corrugation. The vehicle vibration response to these irregularities on the road surface contributes to the road roughness perceived by gravel road users in terms of ride quality (comfort or discomfort). Therefore, it is crucial to maintain gravel roads at an acceptable level of service for improved ride quality and efficient and safe transportation. In particular, corrugations are a safety hazard that causes discomfort to road users and can also cause loss of control of the vehicle, especially at high speeds and when loose gravel surrounds the corrugations. This paper investigates the contribution of corrugations to the mean square value of the squared magnitude of vehicle vibration response and also the interaction between the vehicle speed and the magnitude of the vehicle vibration response. The mean square values of the vehicle vibration response signals are estimated using a moving average low-pass filter, and the signals are split into adjacent frequency sub-bands with the aid of band-pass filters defined based on the Discrete Fourier transform (DFT). The results of the estimated mean square values of the vibration responses and the band-pass filtered vibration responses reveal that corrugations have a more pronounced impact on the magnitude of the mean values of the squared vehicle vibration response than potholes and would thus significantly contribute to the roughness of gravel roads and the perceived ride quality. A positive correlation between vehicle speed and the magnitude of vibration response to gravel road irregularities was also observed, i.e., increasing driving speed resulted in higher vibration magnitudes. These findings show the potential of the study approach for gravel road condition assessment.

During the turning of a workpiece in a lathe, the material deformation process will cause the insert to wear and will, hence, limit the tool life. Commonly, the time an insert is used for machining is limited by a so-called safe time, a conservative bound set to avoid tool failures during machining. By enabling vibration monitoring of how the tool wear progress with low uncertainty, it is likely that the effective time an insert is used may be extended. Understanding tool wear and the ability to determine the tool degradation levels will enable an increased productivity of each insert that, in turn, will cause a significant cost reduction for inserts particularly in automatized manufacturing. A robust way to understand the tool condition may be provided based on the information in tool vibration during turning, i.e., from tool vibration measured and collected from the tool holder during the machining of workpieces. This paper explores the measurement set up and methodology for analyzing the signals collected for the characterization of the tool condition.

Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DMs have the potential to enhance maintenance practices in the maintenance of gravel roads.