Despite the difficulties in obtaining the ultimate capacity of the large diameter bored piles (LDBP) using the in situ loading test, this method is the most recommended by several codes and design standards. However, several settlement-based approaches, alongside the conventional capacity-based design approach for LDBP, are proposed in the event of the impossibility of performing a pile-loading test during the design phase. With that in mind, natural clays usually involve some degree of over consolidation; there is considerable debate among the various approaches on how to represent the behavior of the overconsolidated (OC)stiff clay and its design parameters, whether drained or undrained, in the pile-load test problems. In this paper, field measurements of axial loaded to failure LDBP load test installed in OC stiff clay (Alzey Bridge Case Study, Germany) have been used to assess the quality of two numerical models established to simulate the pile behavior in both drained and undrained conditions. After calibration, the load transfer mechanism of the LDBP in both drained and undrained conditions has been explored. Results of the numerical analyses showed the main differences between the soil pile interaction in both drained and undrained conditions. Also, field measurements have been used to assess the ultimate pile capacity estimated using different methods.

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The full-scale static pile loading test is without question the most reliable methodology for estimating the ultimate capacity of large diameter bored piles (LDBP). However, in most cases, the obtained load-settlement curves from LDBP loading tests tend to increase without reaching the failure point or an asymptote. Loading an LDBP until reaching apparent failure is seldom practical because of the significant amount of settlement usually required for the full shaft and base mobilizations. With that in mind, the supervised learning algorithm requires a huge labeled data set to train the machine properly, which makes it ideal for sensitivity analysis, forecasting, and predictions, among other unsupervised algorithms. However, providing such a huge dataset of LDBP loaded to failure tests might be very complicated. In this paper, a novel practice has been proposed to establish a labeled dataset needed to train supervised machine learning algorithms on accurately predicting the ultimate capacity of an LDBP. A comprehensive numerical parametric study was carried out to investigate the effect of both pile geometrical and soil geotechnical parameters on both the ultimate capacity and settlement of an LDBP. This study was based on field measurements of loaded to failure LDBP tests. Results of the 29 applied models were compared with the calibrated model results, and the variation in LDBP behavior due to change in any of the hyperparameters was discussed. Accordingly, three primary characteristics were identified to diagnose the failure of LDBPs. Those characteristics were utilized to establish a decision tree of a supervised machine learning algorithm that can be used to predict the ultimate capacity of an LDBP.

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Continually using fossil fuels as the main source for producing electricity is one of the main factors causing global warming. Through the past years, several efforts have been made, looking for sustainable, environmentally friendly, and clean energy alternatives. Harvesting geothermal energy from roadway pavement is one of the alternatives that have been developed and investigated recently. Herein, a systematic review and bibliometric analysis were conducted to provide a comprehensive overview of the potentials of harvesting thermal energy from asphalt pavement and to assess the level of achievement being attained towards developed technologies. A total of 713 articles were initially collected, considering the period between 2006 and 2021; later, a series of filtration processes were performed to reach 47 publications. The thermal energy harvesting technologies were categorized into three main sectors, at which their basics and principles were discussed. In addition, a detailed description of the systems’ configurations, materials, and efficiency was presented and described. Finally, gaps and future directions were summarized at the end of this paper. The fundamental knowledge introduced herein can inspire researchers to detect research gaps and serve as a wake-up call to motivate them to explore the high potentials of utilizing pavements as a clean and sustainable energy source.