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Development of pavement design over the past decades has focused on moving from empirical design equations to more powerful and adaptive design schemes. The AASHTO mechanistic–empirical pavement design guide (MEPDG) has been developed to model pavement structure and predict its service life more accurately. Although MEPDG has been widely implemented to design conventional pavement, it is not yet capable of predicting the service life of pavement reinforced with geosynthetics. Given the above concerns, seven full-scale test sections that were constructed at Louisiana Transportation Research Center-Pavement Research Facility (LTRC-PRF) were devoted to a structural experiment to investigate the performance of geosynthetic reinforced pavements. The benefits of using geosynthetics to enhance the performance of pavements constructed over soft subgrades was evaluated using cyclic plate load testing. Cyclic load at a frequency of 0.77 Hz was applied through a 305 mm diameter steel plate. The test results clearly show the benefits of geosynthetics in significantly reducing pavement rutting. The test section with double geosynthetic layers performed better than the other six sections studied. After eliminating the effect of variations in construction, the benefits of geosynthetic reinforcement are quantified within the context of the AASHTOWare Pavement ME Design Guide. The developed design procedure is capable of quantifying the contribution of geosynthetics in pavements to base reinforcement as well as subgrade stabilization. A design methodology is proposed that falls within the context of MEPDG.

The approach slab is constructed at bridge ends to serve as a smooth transition from the highway pavement to the bridge deck. However, motorists usually complain about a sudden change in elevation (bump) at the highway/approach slab (H/S) joint that causes a potential hazard for public safety, damage to vehicles, and rider discomfort. This paper develops a finite element (FE) analysis for the differential settlement at the H/S joint when supported by a strip footing that sits on compacted layers of soil embankment with uniaxial geogrid reinforcement. A parametric study was conducted to select the optimum design that consists of five geogrid layers equally spaced within a depth of 2 [Formula: see text] below the strip footing, where [Formula: see text] is the width of the footing. The inclusion of geogrid reinforcement did not only enhance the ultimate bearing stress of the strip footing but also redistributed the vertical loads over a wider region of soil embankment and thus reduced settlement. A case study is also presented for modeling the performance of a preliminary design proposed by Tennessee Department of Transportation (TDOT) for the retrofit of bridge ends. The FE analysis showed a 30%–40% improvement in the ultimate bearing stress of the strip footing when the geogrid reinforcement proposed by TDOT is extended to a depth of 1.5 [Formula: see text] below the footing.

Several approaches have been developed to estimate the ultimate capacity of piles, such as static and dynamic load tests, static analysis from soil borings, and directly utilizing in-situ test results. Recently, there has been increased interest in using in-situ cone penetration test (CPT) to estimate pile capacity. Several analytical pile-CPT methods have been developed, which involve several correlation assumptions that can affect their accuracy. In this paper, three tree-based machine learning (ML) models, namely decision tree (DT), random forest (RF), and gradient boosted tree (GBT), are developed for estimating the ultimate capacity of piles from CPT data. A database that contains 80 pile load tests and associated CPT data collected in Louisiana was used to develop these ML models. The measured ultimate pile capacity (Qm) was determined using Davisson’s interpretation method from the load–settlement curve of each pile load test. Among the developed ML models, GBT demonstrated the most accurate ML model compared with the others. The estimation of ultimate pile capacity from the GBT model is compared with those obtained from the four best-performing direct pile-CPT methods (based on a previous study): the University of Florida (UF), probabilistic, European Regional Technical Committee 3 (ERTC3), and Laboratoire Central des Ponts et Chaussées (LCPC) methods. The GBT and pile-CPT methods were evaluated and ranked based on analysis of multiple statistical criteria. The results clearly showed that the GBT model outperforms the four direct pile-CPT methods for estimating the ultimate capacity of piles.

Geosynthetic-reinforced pile-supported (GRPS) embankments are widely used in soft soil regions to support roadways. Various design methods for GRPS embankments have been developed; however, engineering experience and recent research have shown that the performance of these design methods varies case by case. This paper systematically evaluates the performance of several empirical GRPS embankment design methods, which include the BS8006 method, the Nordic method, the EBGEO method, the FHWA method, and the CUR226 method. A preliminary assessment of these methods using three field case studies confirms that their performances differ from each other and from field measurements, the differences sometimes being quite significant. To enable a more systematic evaluation of the design methods, a validated finite element model (FEM) was first used to conduct a comprehensive parametric study considering typical soft soil conditions and various geometrical parameters, the results of which served as a baseline for the evaluation of the design methods. The efficacy, stress concentration ratio, maximum differential settlement, and maximum reinforcement tension were used as indicators for the method evaluation. The results show that, overall, the CUR226 method outperforms other design methods. The percentage difference between the CUR226 method and FEM can be as small as 5%, and the maximum value is 130%. It was also found that the Nordic method does not apply to small embankment heights, and that the BS8006 method provides a large overestimation of reinforcement tension when pile spacing is large (>4 ft) for all embankment heights considered.

The cone penetration test (CPT) has been widely used in many geotechnical engineering applications, including soil identification and classification, and evaluation of different soil properties. However, the uncertainties associated with site variation are typical characteristics of subsurface soil conditions that cannot be ignored. Therefore, the effect of site variability on the correlated soil properties from collected field data, such as CPT data, obtained from discrete locations across the site needs to be evaluated. In this study, six well-established spatial interpolation techniques — ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) — were investigated to evaluate the best interpolation method for incorporating site variability. Six CPT test sites were used to evaluate the performance of these spatial interpretation methods. For each site, CPT profiles at specified locations were generated using the different interpolation techniques, and the generated CPT profiles were compared with the measured CPT profiles. The best-fit line of measured versus predicted cone tip resistance (qc) values, mean bias factor (λ), coefficient of variation (COV), and root mean square error (RMSE) were calculated for each CPT profile and used as a criteria for evaluating the different spatial interpolation methods. The results of this study demonstrated the ability of these spatial interpolation methods for generating CPT profiles with good accuracy. The slope of best-fit line of measured versus predicted qc ranges from 0.93 to 0.95, the mean of λ ranges from 0.90 to 0.98, and the COV ranges from 0.34 to 0.53. The IDW, OK, and SK showed the best spatial interpolation methods (in order) for four out of the six CPT sites. For site 4, OK, IDW, and SK showed the best spatial interpolation methods (in order); while for site 5, the three best spatial interpolation techniques are SK, IDW, and OK (in order).

A 2-D finite flement model was developed in this study to conduct a FE parametric study on the effects of some variables in the performance of geosynthetic reinforced soil integrated bridge system (GRS-IBS). The variables investigated in this study include the effect of internal friction angle of backfill material, width of reinforced soil foundation (RSF), secondary reinforcement within bearing bed, setback distance, bearing width and length of reinforcement. Other important parameters such as reinforcement stiffness and spacing were previously investgated by the authors. The performance of GRS-IBS were investgated in terms of lateral facing displacement, strain distribution along reinforcement, and location of potential failure zone. The results showed that the internal friction angle of backfill material has a significant impact on the performance of GRS-IBS. The secondary reinforcement, setback distance, and bearing width have low impact on the performance of GRS-IBS. However, it was found that the width of RSF and length of reinforcement have negligible effect on the performance of GRS-IBS. Finally, the potential failure envelope of the GRS-IBS abutment was found to be a combination of punching shear failure envelope (top) that starts under the inner edge of strip footing and extends vertically downward to intersect with Rankine active failure envelope (bottom).

Load tests of drilled shafts are often performed using Osterberg cell (O-cell) testing, a popular load test method for drilled shafts, which measures both side and tip resistance. However, it is common that only one of the resistance components can be fully mobilized. Therefore, extrapolation of the partially mobilized resistance is often required to determine the total resistance or the equivalent top-down curve. The extrapolation tends to introduce errors to the constructed total resistance values, which subsequently affect the calibrated resistance factors required for the LRFD design of drilled shafts. In this study, eight O-cell tests of drilled shafts with total measured resistances close to the failure criteria defined by FHWA, 5% of the shaft diameter (B), were collected among 64 drilled shaft load tests from Louisiana and Mississippi. For each of the eight cases, extrapolation was performed on both tip and side movement curves for the construction of the equivalent top-down load-settlement (ELT) curves. Data points from the measured side or tip movement curve were removed systematically to create a total of 80 cases with partially mobilized movement curves, and extrapolation exercises were performed on each fabricated case to obtain its equivalent top-down curve. The error of bias for each fabricated case was determined for statistical analyses. Multiple linear regression analysis was performed on the bias errors to model the bias errors caused by extrapolation. Calibrated resistance factors were determined and compared between the original database and fabricated database needing extrapolation. A correction method is proposed, based on a linear regression relationship, to estimate and minimize the extrapolation error of bias for less mobilized databases.

Pile setup is defined as the increase in pile capacity with time. This increase in pile capacity starts immediately after end of driving (EOD). Majority of the setup conducted during the consolidation period followed by a small amount during the post-consolidation period. However, there is no available methodology to estimate the duration of setup during the consolidation period, and to establish a theoretical upper time limit for pile setup capacity beyond which pile setup capacity can be neglected. This paper examines or focuses to develop a methodology to estimate the time duration of pile setup. Three instrumented test piles (TPs) were driven mainly in clayey soil in Louisiana besides regular bridge construction projects. The piles were instrumented with sister bar strain gages to measure the load transfer along the piles’ length, and four sets of pressure cells and piezometers were installed on each pile face to measure the total stress, effective stress and excess pore water pressure (PWP) with time. Dynamic and Static load tests were performed for a duration of 6 months after EOD and hence study setup. Piezocone dissipation tests were performed at each TP locations at different depths to measure the horizontal coefficient of consolidation (ch) besides the regular laboratory and in situ cone penetration test (CPT) to characterize the subsurface soil condition. The piezometers that were installed on the pile face showed that the dissipation continued from 2.5 h to 90 days after EOD depending on the soil properties. The equation that was proposed by Teh and Houlsby (Geotechnique 41(1):17–34, 1991) for dissipation test of piezocone was adopted to calibrate the time factor for pile. A new modified time factor for pile (Tpile*) was proposed in this study to estimate the duration of pile setup.

In geotechnical design practices, the undrained shear strength of soil is regarded as one of the engineering properties of paramount importance. Over the past years, several theoretical and empirical methods have been developed to estimate the undrained shear strength based on soil properties using in-situ tests such as cone and piezocone penetration tests (CPT and PCPT). However, most of these methods involve correlation assumptions that can result in inconsistent accuracy. In this paper, an artificial neural network (ANN) is used to devise a model with a better and more consistent prediction of the undrained shear strength of soil from CPT data. The ANN algorithm does not require such assumptions as it learns from previous cases/instances. A database was prepared of soil boring data and laboratory test data along with corresponding CPT/PCPT data from 70 test sites located in 14 different parishes in Louisiana. Presenting this data to the ANN, models were trained through trial and error using different network algorithms, such as back propagation method and quasi-Newton method. Different ANN models were trained using corrected cone tip resistance and sleeve friction input data along with some other easily measurable soil properties. The results of ANN models were then compared with a conventional empirical method of determining undrained shear strength of soil from CPT parameters. The results of this study clearly demonstrated that the ANN models outperformed the conventional empirical method, which confirms the applicability of ANN in the evaluation of the undrained shear strength of soil from CPT data.

The cone and piezocone penetration tests (CPT, PCPT) have been widely acknowledged as useful in-situ testing tools for subsurface investigation, characterization of soil type, and evaluation of different soil properties. Because of similarity between the cone and pile, the evaluation of axial pile capacity was one of initial applications of the CPT/PCPT. A previous study conducted by the authors on 80 pile load tests of precast prestressed concrete (PPC) piles demonstrated that some pile-CPT methods are able to predict the ultimate axial pile capacity with better accuracy than other methods. These methods include: Schmertmann, De Ruiter and Beringen, Laboratoire Central des Ponts et Chaussées (LCPC), European Regional Technical Committee 3 (ERTC3), University of Western Australia (UWA), probabilistic, and University of Florida (UF) methods. The results of these seven pile-CPT methods were compared and their performance was examined for different soil categories where different percentages of pile capacity contribution is because of sandy layers. The log-normal distribution of the estimated to measured pile capacity for these pile-CPT methods was adopted to develop combined pile-CPT methods that optimize the estimation accuracy of axial pile capacity in different soil categories. Reliability analysis using Monte Carlo Simulation (MCS) was used to evaluate the resistance factors ( ϕ) and efficiency ( ϕ/ λR) of the individual and combined pile-CPT methods. Results of analysis of 80 pile load tests demonstrated the advantage of using the combined pile-CPT methods over the individual methods in relation to improving the accuracy of estimating the ultimate axial pile capacity and having better resistance factors.

In this study, the effects of pile spacing and pile group (PG) configuration on the static lateral resistance of PGs are investigated using finite element (FE) numerical modeling. Three-dimensional...

In order to incorporate the influence of collected in-situ data, the spatial correlation between the data and the foundation needs to be explored. Statistical information of the soil property can be estimated from available field data obtained from testing at discrete locations across the site. In this research, several well-established spatial interpolation methods like ordinary kriging (OK), simple kriging (SK), inverse distance weight (IDW), spline, natural neighbor (NaN), and universal kriging (UK) were incorporated to evaluate the best method for generating synthetic cone penetration test (CPT) data. To remove the spikes, continuous five points averaging was done to generate the smoothed tip resistance. For the analysis, the spatial interpolation was performed in each foot (depth wise). Six CPT cases were investigated in this study. According to the results, four out of six cases, if the first priority is given to bias factor followed by coefficient of variation (COV) and root mean square error (RMSE), the best three spatial interpolation techniques are IDW, OK, and SK sequentially, based on their performance. For the other two cases, in one case, the best three spatial interpolation techniques are OK, IDW, and SK, sequentially, and the other case shows SK, IDW, and OK sequentially are the best three spatial interpolation techniques.