Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach?

Academic and practitioner interest in the physical performance of male professional soccer players in the competition setting determined via time-motion analyses has grown substantially over the last four decades leading to a substantial body of published research and aiding development of a more systematic evidence-based framework for physical conditioning. Findings have forcibly shaped contemporary opinions in the sport with researchers and practitioners frequently emphasising the important role that physical performance plays in match outcomes. Time-motion analyses have also influenced practice as player conditioning programmes can be tailored according to the different physical demands identified across individual playing positions. Yet despite a more systematic approach to physical conditioning, data indicate that even at the very highest standards of competition, the contemporary player is still susceptible to transient and end-game fatigue. Over the course of this article, the author suggests that a more pragmatic approach to interpreting the current body of time-motion analysis data and its application in the practical setting is nevertheless required. Examples of this are addressed using findings in the literature to examine: a) the association between competitive physical performance and ‘success’ in professional soccer, b) current approaches to interpreting differences in time-motion analysis data across playing positions and, c) whether data can realistically be used to demonstrate the occurrence of fatigue in match-play. Gaps in the current literature and directions for future research are also identified

ROADS—Rover for Bituminous Pavement Distress Survey: An Unmanned Ground Vehicle (UGV) Prototype for Pavement Distress Evaluation

Maintenance has a major impact on the financial plan of road managers. To ameliorate road conditions and reduce safety constraints, distress evaluation methods should be efficient and should avoid being time consuming. That is why road cadastral catalogs should be updated periodically, and interventions should be provided for specific management plans. This paper focuses on the setting of an Unmanned Ground Vehicle (UGV) for road pavement distress monitoring, and the Rover for bituminOus pAvement Distress Survey (ROADS) prototype is presented in this paper. ROADS has a multisensory platform fixed on it that is able to collect different parameters. Navigation and environment sensors support a two-image acquisition system which is composed of a high-resolution digital camera and a multispectral imaging sensor. The Pavement Condition Index (PCI) and the Image Distress Quantity (IDQ) are, respectively, calculated by field activities and image computation. The model used to calculate the I-ROADS index from PCI had an accuracy of 74.2%. Such results show that the retrieval of PCI from image-based approach is achievable and values can be categorized as "Good"/"Preventive Maintenance", "Fair"/"Rehabilitation", "Poor"/"Reconstruction", which are ranges of the custom PCI ranting scale and represents a typical repair strategy

Cold asphalt contaning 100% reclamed asphalt. A sustainable technology for cycle paths and maintenance intervations

Both the National Recovery and Resilience Plan (Next Generation EU Program) and the development strategies for Smart Cities focus on cycle and pedestrian paths. Their pavements must be safe, durable and sustainable and considering the need to preserve the resources that Planet Earth offers to humans, it is essential to opt for innovative construction technologies that allow recycling methods without necessarily involving the addition of first-use materials. In the field of road infrastructure, the recovery of material deriving from the demolition of old pavements (RA - Reclaimed Asphalt) is only possible thanks to the use of specific products. A state-of-the-art rejuvenator is currently being used for the construction of cycling paths with 100% cold-mixed RA. This product is currently being studied for the INFRAROB project: “Maintaining integrity, performance and safety of the road infrastructure through autonomous robotized solutions and modularization” (Horizon 2020) with particular reference to “potholes patching” materials. Some technical data of the experiences developed to date are shown below

The reliability, validity and sensitivity of a novel soccer-specific reactive repeated-sprint test (RRST).

PURPOSE: The aim of this study was to determine the reliability, validity and sensitivity of a reactive repeated-sprint test (RRST). METHODS: Elite (n = 72) and sub-elite male (n = 87) and elite female soccer players (n = 12) completed the RRST at set times during a season. Total distance timed was 30 m and the RRST performance measure was the total time (s) across eight repetitions. Competitive match running performance was measured using GPS and high-intensity running quantified (≥ 19.8 km h(-1)). RESULTS: Test-retest coefficient of variation in elite U16 and sub-elite U19 players was 0.71 and 0.84 %, respectively. Elite U18 players' RRST performances were better (P < 0.01) than elite U16, sub-elite U16, U18, U19 and elite senior female players (58.25 ± 1.34 vs 59.97 ± 1.64, 61.42 ± 2.25, 61.66 ± 1.70, 61.02 ± 2.31 and 63.88 ± 1.46 s; ES 0.6-1.9). For elite U18 players, RRST performances for central defenders (59.84 ± 1.35 s) were lower (P < 0.05) than full backs (57.85 ± 0.77 s), but not attackers (58.17 ± 1.73 s) or central and wide midfielders (58.55 ± 1.08 and 58.58 ± 1.89 s; ES 0.7-1.4). Elite U16 players demonstrated lower (P < 0.01) RRST performances during the preparation period versus the start, middle and end of season periods (61.13 ± 1.53 vs 59.51 ± 1.39, 59.25 ± 1.42 and 59.20 ± 1.57 s; ES 1.0-1.1). Very large magnitude correlations (P < 0.01) were observed between RRST performance and high-intensity running in the most intense 5-min period of a match for both elite and sub-elite U18 players (r = -0.71 and -0.74), with the best time of the RRST also correlating with the arrowhead agility test for elite U16 and U18 players (r = 0.84 and 0.75). CONCLUSION: The data demonstrate that the RRST is a reliable and valid test that distinguishes between performance across standard, position and seasonal period

Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement

Automatic crack detection on road pavements using encoder-decoder architecture

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low-level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms

Materials study to implement a 3D printer system to repair road pavement potholes

InfraRob is a research project funded by the European Commission's research programme Horizon 2020 that aims to maintain integrity, performance, and safety of the road infrastructure through autonomous robotized solutions and modularization. A specific task of the project is focused on the development of a system 3D printer able to extrude a specific mixture for filling in small cracks and potholes, to be integrated with an existing small autonomous carrier. The first step of the research deals with the definition of the optimal parameters of the system 3D printer/mixture, by studying in parallel the material design and the printer design. This paper presents the study performed on a mixture chosen among those commonly used for road potholes repair. The mixture is studied to achieve and balance the different conflicting performances: consistence, flowability homogeneity, and internal structure. In addition to the basic components, the use of special additives has also been explored to improve the plasticity and adhesivity of the mixture. The first phase of tests is conducted to define the main printing controls: i) Extrudability control: materials for 3D printing need to have an acceptable degree of extrudability, which is related to the capacity of a material to pass continuously through the printing head; ii) Flowability control, to ensure the mixture can be easy-pumpable in the delivery system and easy-usable on the crack or the pothole to be filed-in; iii) Setting time control: printing material requires a certain setting time to maintain a consistent flow rate for good extrudability, thus appropriate additives are needed to control the setting time. The second phase includes in situ tests to verify the compaction of the mixture under the traffic loads. The paper presents the results of the lab and in situ tests, and the features of the chosen mix, suitable to be managed by the 3D printer

Eco-efficient asphalt recycling for urban slow mobility

Cycling infrastructures contribute to advancing zero-impact transport systems, aligning with the European Commission’s proactive climate change mitigation policies. This paper explores the potential of innovative and sustainable pavements for cycling paths with mixtures composed of road-milling materials. This investigation involves low-environmental-impact bituminous-based mixtures differing from recipe, mixing method, and laying. Up to 100% secondary aggregates are used as alternative materials to design the Grande Raccordo Anulare delle Biciclette (GRAB), a 44-km cycling ring in Rome. According to the European standard EN 15804, their “from cradle to gate” life cycle analysis allows a comprehensive assess- ment and comparison of the environmental impact. Core and additional environmental impact categories and resource use indicators were quantified using primary data from asphalt producers and secondary data from the Ecoinvent database in the SimaPro software. Within the H2020 InfraROB project (grant agreement no. 955337), which aims at enhancing road infrastructure integrity, performance, and safety through autonomous robotic solutions and modularization, experimental sections have been constructed using a cold-mixed asphalt composed entirely of recycled asphalt and a rejuvenating addi- tive. The results underscore the potential of the examined low-impact approach in conserving Earth’s resources, ensuring long-lasting infrastructure for vulnerable urban populations and fostering sustainable environmental management