Over the past few decades, premature deterioration of reinforced concrete structures exposed to severe environmental actions and mechanical loading has become a serious problem. Previous studies have shown that the use of ultra-high performance fiber reinforced concrete (UHPFRC) improves the structural response and extends the durability of concrete structures. In this study, the flexural behavior of reinforced concrete beams retrofitted with UHPFRC is investigated and experimental results are compared with 3-D finite element analysis.The experiments were performed on reinforced concrete beams repaired in tension and compression zone, with UHPFRC of varying thicknesses. The flexural strength of repaired beams was investigated by four-point bending test and compared with that of reference beam without repair. Experimental and analytical results indicate that the ultimate flexural strength of RC beams repaired with UHPFRC in tension and compression zone is increased, with the increase of UHPFRC thickness. Thereafter, a parametric study was carried out by using MSC/Marc simulation to investigate the influence of tensile properties of UHPFRC and yield strength of tension steel on the flexural capacity of repaired beams. The investigation shows that the UHPFRC improves stiffness and delay the formation of localized cracks, thus, improving the resistance and durability of repaired beams.

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

<abstract> <p>This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.</p> </abstract>

According to the mission scenario of the International Space Exploration Collaboration Group (ISECG), a "Mars sample return plan" is underway to bring the soil of Mars back to Earth by 2030. In the near future, in order to efficiently perform various operations on Mars (filling, excavation, sampling, ground improvement, etc.), presimulation on Earth will be important. Therefore, there is an urgent need to develop an appropriate simulated soil and to understand its material and mechanical properties. Based on this background, the authors are investigating the physical and basic mechanical properties of Mojave Mars Simulant soil (MMS-1), which has been reported as a simulated soil for Mars. This study focused on the particle shape of MMS-1 and was conducted to investigate the effect of particle shape on shear strength and volume change. As a result, it was revealed that MMS-1 has a higher shear strength than Toyoura sand and has a clear peak as the relative density increases. Moreover, MMS-1 is a material that causes volume expansion during shearing even for low stress and density states compared with artificial soil, and it was revealed that this is due to the angularity of the MMS-1 particles.

Thin-bed sequences, especially those made up of alternating weak and strong sand layers with thickness of less than 1-ft, present a particular challenge in the generation of mechanical earth models (MEMs) for geomechanics design and analyses. Because these beds are thinner than the tool resolution, their heterogeneity and the variations between adjacent layers are often undetected by logging tools normally used to acquire geomechanics data. In other words, these tools cannot resolve the existence of alternating strong and weak sequences; they provide only averaged mechanical properties across these sequences. In one such case, an MEM and sanding study based on wireline sonic data indicated no tendency for sanding across a thick reservoir interval at the drawdowns required for economic oil production. The somewhat flat, log-derived curve of unconfined compressive strength (UCS) indicated that the entire reservoir was above the critical sand-free UCS state; however, laboratory data had revealed a large spread in UCS values, including rock strength values that are indicative of a significant sanding risk. In fact, the field had a history of solids production with depletion. Using the high-resolution images (with vertical resolution of 0.2 in.) provided by a microresistivity imaging tool across the same thin-bed intervals, and using alpha processing and other petrophysical log enhancement to further improve data interpretation, it was possible to generate a high-resolution (around 0.2 in.) porosity log. These downscaling techniques were combined with an empirical UCS-porosity relationship obtained from the available laboratory data. The resulting downscaled UCS log exhibited the wide range of strength values that were known to exist across the thin-bed sequence and were consistent with the history of sand production in the field. The remaining sanding evaluation for the well was completed using this integrated petrophysics and geomechanics approach. When the MEM and sanding analyses were extended to other wells in this field, the results matched well with the actual depletion curves and sanding history data. Introduction The Sarir field in Libya, operated by the Arabian Gulf Oil Company (AGOCO), has been experiencing progressively worse sand production problems in an increasing number of wells since 1984.[1,2] A geomechanics and sanding study initiated in 2004 investigated the probable cause of this problem and provided the information and interpretations needed to select an appropriate sand management solution, assist in future development decisions, allow appropriate completions planning, and optimize future reservoir management. To this end, the results of a data audit identified what geomechanics-related information existed that might be used to characterize previous sanding problems and diagnose their root causes. A laboratory testing programme using cores from the Sarir field obtained measurements of rock mechanics parameters relevant to stress modelling and sanding prediction. [3] Integrating laboratory results with other geomechanics information available from log, field, and drilling measurements provided an initial MEM. This MEM contained most of the information necessary for subsequent geomechanics analyses and sanding evaluations.[4,5] However, one significant finding was that routine UCS determinations from sonic logs in the Sarir field were unable to detect thin weak zones having thicknesses below the tool vertical resolution (i.e., 42 in.) of the sonic wireline tool. Therefore, petrophysical log-enhancement processing6,7 was conducted to obtain high-resolution, downscaled porosity logs using microresistivity image data. These data were then used to derive high-resolution (i.e., downscaled) UCS logs through UCS-porosity correlations. The UCS data from this process, which exhibited the same strength heterogeneities observed in the core data, provided the improved description of rock strength in the MEM that was needed in the subsequent sanding analyses of the thin-bed sequences.

The disposal of 2011 Japan earthquake waste has become an important issue in Japan and it is not realistic or economical to send all of these wastes to landfill sites, due to limited space, high costs, and related environmental issues. In sustainable geotechnical applications, mixing of the separated soils from disaster wastes with additives (e.g., cement and fiber) is required to improve their strength and stiffness characteristics. In this study, monotonic triaxial drained compression tests are performed on medium dense specimens of Toyoura sand-cement-fiber mixtures with different percentages of fiber and cement (e.g., 0–3%) additives. The experimental results indicate that behavior of the mixtures is significantly affected by the concentration of fiber and cement additives. Based on a comprehensive set of test results, modifications to the series of equations were developed that can be used to evaluate the shear modulus and mobilized stress curves at small-strain levels. The experimental results and model comparison show that the elastic threshold strain (γe), reference strain (γr), increases with fiber and cement additives. In addition, the range of curvature parameter, from 0.88 to 1.0, provides a good comparison with the results of small-strain measurements. Overall, the comparison of the results and model shows that the small-strain measurements obtained using local strain transducers fall within the range of model upper and lower bound curves. The results of the unreinforced, fiber, and cemented sand shows a close agreement with the model mean curve, but fiber-reinforced cemented sand shows a good comparison with model upper bound.

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction.

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