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The earth pressure balance tunnel boring machine (TBM) is advanced excavation machinery used to efficiently drill through subsurface ground layers while placing precast concrete tunnel segments. They have become prevalent in tunneling projects because of their adaptability, speed, and safety. Optimal usage of these machines requires information and data about the soil of the worksite that the TBM is drilling through. This paper proposes the utilization of artificial intelligence and machine learning, particularly recurrent neural networks, to predict the operational parameters of the TBM. The proposed model utilizes only performance data from excavation segments before the location of the machine as well as its current operating parameters to predict the as-encountered parameters. The proposed method is evaluated on a dataset collected during a tunneling project in North America. The results demonstrate that the model is effective in predicting operation parameters. To address the potential issue of gathering sufficient data to retrain the model, the possibility of transferring the trained model from one tunnel to another is tested. The results suggest that the model is capable of performing accurately with minimal or even no re-training.

Study Design: Description and evaluation of a novel surgical training platform. Objectives: The purpose of this study was to investigate the face, content, and construct validity of 5 novel surgical training models that simulate freehand and percutaneous (minimally invasive surgery [MIS]) pedicle screw placement. Methods: Five spine models were developed by residents: 3 for freehand pedicle screw training (models A-C) and 2 for MIS pedicle screw training (models D and E). Attending spine surgeons evaluated each model and, using a 20-point Likert-type scale, answered survey questions on model face, content, and construct validity. Scores were statistically evaluated and compared using means, standard deviations, and analysis of variance between models and between surgeons. Results: Among the freehand models, model C demonstrated the highest overall validity, with mean face (15.67 ± 5.49), content (19.17 ± 0.59), and construct (18.83 ± 0.24) validity all measuring higher than the other freehand models. For the MIS models, model D had the highest validity scores (face, content, and construct validity of 11.67 ± 3.77, 18.17 ± 2.04, and 17.00 ± 3.46, respectively). The 3 freehand models differed significantly in content validity scores ( P = .002) as did the 2 MIS models ( P < .001). The testing surgeons’ overall validity scores were significantly different for models A ( P = .005) and E ( P < .001). Conclusions: A 3-dimensional-printed spine model with incorporated bone bleeding and silicone rubber soft tissue was scored as having very high content and construct validity for simulating freehand pedicle screw insertion. These data has informed the further development of several surgical training models that hold great potential as educational adjuncts in surgical training programs.

Managing pavements requires a reliable pavement performance indicator. The current study examines the feasibility of using the impulse response (IR) method to provide a reliable, mechanistic measure of current and future pavement performance. In an extension of the pavement family approach, IR-inferred dynamic stiffness (kd) decay models were generated for pavements with asphalt concrete (AC) and portland cement concrete (PCC) surfaces. AC pavement family subdivisions devised with the total thickness of structurally capable layers were found to yield the most significant correlations. Grouping data from all IR tests on PCC pavement sections into one family yielded a coefficient of determination of large significance with low scatter in the data. A statistically significant correlation could not be established for AC pavement between IR-estimated kd and the low-strain modulus (ESEIS) inferred by the method of spectral analysis of surface waves. In PCC pavements, a weak correlation was observed between kd and ESEIS. Though the sample used for comparison with structurally distressed sections was small, results suggested that in AC pavements, visually inferred traffic-related distresses did not correlate with structural failure. For PCC pavements, visual identification of structural distresses was more accurate than for AC pavements.

Roller-integrated continuous compaction control can improve earthwork quality assurance in the United States; therefore, it is receiving considerable attention. Most preliminary specifications that are being evaluated are based on specifications from Austria, Germany, and Sweden. In general, the European specifications encompass either of two approaches: (a) spot testing in roller-identified weak areas or (b) calibrating roller-measured values to spot test values. A pilot implementation on an active earthwork construction site in Colorado showed spot testing roller-identified weak zones represented an incremental progression from the current methods employed in the United States. The calibration option was shown to be labor intensive, and the results presented here indicate that the initial time investment may not be justifiable. Implementation of the calibration option is further complicated because it is a significant departure from current methods. An analysis of the spot-testing option showed that if a similar level of quality acceptance is desired, then current requirements for quality assurance based on density spot testing may have to be lowered for quality assurance to be based on continuous compaction control.

Nondestructive tests to estimate stiffness modulus, such as the lightweight deflectometer (LWD), have experienced increased popularity, but very little research has been performed to evaluate the LWD with radial sensors. Results are presented from LWD testing with radial sensors that measured the deflection bowl on one- and two-layer field test beds consisting of unbound materials. LWD testing produced a measurable deflection bowl on medium-stiffness granular materials to a radial sensor spacing of 750 mm (30 in). When limited to a stiff over soft layered system, the LWD with radial sensors demonstrated the ability to backcalculate layered moduli accurately. Backcalculated moduli closely matched laboratory-determined moduli from triaxial testing at a similar stress state as in the field. The measurement depth for the LWD with radial sensors was found to be 1.8 times plate diameter versus the measurement depth of conventional LWD testing of 1.0 to 1.5 times plate diameter. The LWD with radial sensors was able to measure deeper-than-conventional LWD testing because the radial geophones measure vertical surface deflections caused almost entirely by strains in deeper material. Compared with other configurations, the 300-mm and 600-mm (12-in. and 24-in.) radial sensor configuration is recommended for unbound materials because it produces the most accurate moduli backcalculation results and captures deflections critical to the backcalculation process.

Vibratory roller-based measurement of soil stiffness during intelligent compaction and continuous compaction control can be used for performance-based quality assurance of unbound materials. To realize this potential, the relationship between the roller-measured soil stiffness and the soil modulus, particularly resilient modulus, must be understood. The in situ stress states and paths experienced by soil beneath a vibratory roller were compared with stress states and paths during laboratory resilient modulus testing. Observed stress fields within the 1-m depth of influence varied considerably for both vertically homogeneous embankment soil and layered base over subgrade conditions. During low excitation force vibration, roller-induced levels of deviator stress were notably greater than those used during laboratory resilient modulus testing, whereas levels of mean stress were less. Predicted modulus variation with depth was strongly influenced by modulus function parameters. With typical granular soil parameters, modulus was found to be constant with depth for the embankment conditions. With modulus parameters of more fine-grained behavior, modulus increased considerably with depth.

The objective of the present study is to review hemorheological disorders in diabetes mellitus. Several key hemorheological parameters, such as whole blood viscosity, erythrocyte deformability, and aggregation, are examined in the context of elevated blood glucose level in diabetes. The erythrocyte deformability is reduced, whereas its aggregation increases, both of which make whole blood more viscous compared to healthy individuals. The present paper explains how the increased blood viscosity adversely affects the microcirculation in diabetes, leading to microangiopathy.

Vibration-based health monitoring (VBHM) has successfully been used to assess the structural damage to bridges, buildings, aircraft, and rotating machinery. There is significant incentive to apply VBHM techniques to the damage detection and conditional assessment of earth structures (geostructures), e.g., foundations, dams, embankments, and tunnels, to improve design, construction, and performance. An experimental program was carried out to explore the efficacy of VBHM of earth structures. A vibratory roller compactor, instrumented with triaxial accelerometers to continuously measure drum and frame vibrations, was operated on a number of underlying material structures with varying properties. Time-domain and frequency-domain analyses of the coupled machine/earth structure response were performed to glean machine vibration features that reflect changes in underlying structural properties. Results illustrate that drum and frame acceleration amplitudes were fairly insensitive to changes in underlying media stiffness; however, drum acceleration frequency components (harmonics) were found to be sensitive to changes in underlying media and changes in soil properties during compaction. The strata underlying the soil undergoing compaction was found to strongly affect drum vibration characteristics.

The Oklahoma airfield pavement management system (APMS) is a set of pavement management tools that can assist with pavement condition evaluation, as well as prioritization and scheduling of pavement maintenance and rehabilitation activities. Pavement performance models were developed to support the APMS for more than 70 Oklahoma general aviation airports. The family modeling method based on the pavement condition index was tailored to fit the deterioration characteristics of these airfield pavements. The statistical and engineering significance of seven levels of pavement factors was investigated, and pavement factors that affect pavement deterioration significantly were identified as family variables. Asphalt concrete pavement families were formed by sorting pavement function, distress cause, and pavement thickness, while portland cement concrete pavements were divided into families according to pavement function and climate zone. The family polynomial curves were able to reveal the expected deterioration patterns and are logical in engineering principle. Rooted by an adaptive database, the system accepts expert opinion and automatically integrates effects of major maintenance and rehabilitation activities into modeling. From the up-to-date database, the performance models update forecasts automatically.