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Purpose: This study explores the intricate interplay between sentiment extracted from Board of Directors (BOD) statements and the traditional financial metrics of banks. It investigates the relationship between a bank’s financial performance, measured by various financial ratios, and the sentiment expressed in BOD statements. Design/methodology/approach: Drawing from a dataset encompassing BOD statements from 15 Jordanian banks spanning 2017 to 2021, sentiment analysis techniques are employed to derive sentiment scores. These scores are then juxtaposed with principal components distilled from financial metrics to examine their predictive relationship. Findings: Preliminary findings reveal significant non-linear patterns between Hu and Liu’s (2004) sentimental analysis (Lui_sen), the Data Science Lab’s (n.d.) multilingual sentiment tool (multi_sen), and the principal components. A subsequent random-effects panel regression further elucidates these relationships, highlighting the significant influence of specific principal components on sentiment scores. This study provides a compelling case for integrating sentiment analysis with traditional financial indicators, offering a more comprehensive toolkit for evaluating bank performance. Research limitations/implications: Future research should explore broader datasets across multiple sectors, to validate the generalisability of the findings beyond the banking sector in Jordan. Practical implications: This research holds implications for investors, analysts, and stakeholders by combining sentiment analysis with traditional financial metrics. It offers deeper insights into the health of banks and the intentions of management. This integrated approach allows stakeholders to make informed decisions and mitigate risks based on a more comprehensive view of banks' financial situations. Additionally, the possibility of real-time sentiment analysis enables the dynamic monitoring of banking institutions' financial health as updated data becomes available. Social implications: The study’s results are valuable to users of financial information, aiding the Association of Banks and the Central Bank of Jordan in formulating policies for the banking sector. Auditors, consultants, and regulatory bodies in Jordan can also benefit from these insights. Originality/value: This is the first study exploring the relationship between sentiment extracted from BOD statements and traditional financial metrics in Jordan. It provides novel evidence of this relationship, contributing to the literature on the integration of textual data analysis and financial performance.

Implantable devices have successfully proven their reliability and efficiency in the medical field due to their immense support in a variety of aspects concerning the monitoring of patients and treatment in many ways. Moreover, they assist the medical field in disease diagnosis and prevention. However, the devices’ power sources rely on batteries, and with this reliance, comes certain complications. For example, their depletion may lead to surgical interference or leakage into the human body. Implicit studies have found ways to reduce the battery size or in some cases to eliminate its use entirely; these studies suggest the use of biocompatible harvesters that can support the device consumption by generating power. Harvesting mechanisms can be executed using a variety of biocompatible materials, namely, piezoelectric and triboelectric nanogenerators, biofuel cells, and environmental sources. As with all methods for implementing biocompatible harvesters, some of them are low in terms of power consumption and some are dependent on the device and the place of implantation. In this review, we discuss the application of harvesters into implantable devices and evaluate the different materials and methods and examine how new and improved circuits will help in assisting the generators to sustain the function of medical devices.

Background: Limited data exists to support the benefit from second-line chemotherapy in patients with metastatic urothelial carcinoma. Factors that predict survival following progression after first-line platinum-based regimens in patients treated outside clinical trials are not clear. This study intends to evaluate different prognostic factors and the impact of second-line chemotherapy on survival. Methods: We retrospectively reviewed patients with metastatic urothelial carcinoma who experienced disease progression following first-line platinum-based regimens for metastases. These patients received treatment and follow up visits at a single institution. The effect of demographic, disease characteristics, and second-line therapy on overall survival was examined through univariate and multivariate cox-regression analyses. Results: There were 64 patients included. A total of 27 (42%) patients did not receive second-line chemotherapy because of poor Eastern Cooperative Oncology Group performance status, 20 (31%) received combination chemotherapy (platinum-based in 17), and 17 (27%) received a single agent chemotherapy. The median overall survival from the date of documented progression after first-line therapy was 5.0 months. In multivariate analysis, a correlation existed between poor overall survival and performance status of ≥1 (HR: 5.74, 95% CI: 1.4- 45.57, P=0.036), no second-line chemotherapy (HR: 2.72, 95% CI: 1.39-5.31, P=0.003), and ≥2 metastatic sites (HR: 5.19, 95% CI: 1.74-15.44, P

Purpose: This study explores the intricate interplay between sentiment extracted from Board of Directors (BOD) statements and the traditional financial metrics of banks. It investigates the relationship between a bank's financial performance, measured by various financial ratios, and the sentiment expressed in BOD statements. Design/methodology/approach: Drawing from a dataset encompassing BOD statements from 15 Jordanian banks spanning 2017 to 2021, sentiment analysis techniques are employed to derive sentiment scores. These scores are then juxtaposed with principal components distilled from financial metrics to examine their predictive relationship. Findings: Preliminary findings reveal significant non-linear patterns between Hu and Liu's (2004) sentimental analysis (Lui_sen), the Data Science Lab's (n.d.) multilingual sentiment tool (multi_sen), and the principal components. A subsequent random-effects panel regression further elucidates these relationships, highlighting the significant influence of specific principal components on sentiment scores. This study provides a compelling case for integrating sentiment analysis with traditional financial indicators, offering a more comprehensive toolkit for evaluating bank performance. Research limitations/implications: Future research should explore broader datasets across multiple sectors, to validate the generalisability of the findings beyond the banking sector in Jordan. Practical implications: This research holds implications for investors, analysts, and stakeholders by combining sentiment analysis with traditional financial metrics. It offers deeper insights into the health of banks and the intentions of management. This integrated approach allows stakeholders to make informed decisions and mitigate risks based on a more comprehensive view of banks' financial situations. Additionally, the possibility of real-time sentiment analysis enables the dynamic monitoring of banking institutions' financial health as updated data becomes available. Social implications: The study's results are valuable to users of financial information, aiding the Association of Banks and the Central Bank of Jordan in formulating policies for the banking sector. Auditors, consultants, and regulatory bodies in Jordan can also benefit from these insights. Originality/value: This is the first study exploring the relationship between sentiment extracted from BOD statements and traditional financial metrics in Jordan. It provides novel evidence of this relationship, contributing to the literature on the integration of textual data analysis and financial performance. [ABSTRACT FROM AUTHOR]

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 ([q.sub.c]) values, mean bias factor ([lambda]), 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 [q.sub.c] ranges from 0.93 to 0.95, the mean of A 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). Key words: cone penetration test (CPT), site variability, spatial interpolation techniques, statistical analysis. Le test de penetration du cone (CPT) a ete largement utilise dans de nombreuses applications d'ingenierie geotechnique, y compris l'identification et la classification des sols, et l'evaluation des differentes proprietes des sols. Cependant, les incertitudes associees aux variations de site sont des caracteristiques typiques de l'etat du sol souterrain qui ne peuvent etre ignorees. Il faut donc evaluer l'effet de la variabilite du site sur les proprietes du sol correlees a partir des donnees recueillies sur le terrain, telles que les donnees CPT, obtenues a des endroits discrets sur le site. Dans cette etude, six techniques d'interpolation spatiale bien etablies, comme le krigeage ordinaire (OK), le krigeage simple (SK), le krigeage universel (UK), le poids inverse de la distance (IDW), la spline, et le voisinage naturel (NaN) ont ete etudiees pour evaluer la meilleure methode d'interpolation pour incorporer la variabilite du site. Six sites de test CPT ont ete utilises pour evaluer la performance de ces methodes d'interpretation spatiale. Pour chaque site, des profils de CPT ont ete generes a des endroits specifiques en utilisant les differentes techniques d'interpolation, et les profils de CPT generes ont ete compares aux profils de CPT mesures. La ligne de meilleur ajustement des valeurs de resistance en pointe du cone ([q.sub.c]) mesurees par rapport aux valeurs predites, le facteur de biais moyen ([lambda]), le coefficient de variation (COV) et l'erreur quadratique moyenne (RMSE) ont ete calcules pour chaque profil CPT et utilises comme criteres pour evaluer les differentes methodes d'interpolation spatiale. Les resultats de cette etude ont demontre la capacite de ces methodes d'interpolation spatiale a generer des profils de TPC avec une bonne precision. La pente de la ligne de meilleur ajustement du [q.sub.c] mesure par rapport au [q.sub.c] predit varie de 0,93 a 0,95, la moyenne de A varie de 0,90 a 0,98, et le COV varie de 0,34 a 0,53. L'IDW, l'OK et la SK ont montre les meilleures methodes d'interpolation spatiale (dans l'ordre) pour quatre des six sites de CPT. Pour le site 4, OK, IDW et SK ont montre les meilleures methodes d'interpolation spatiale (dans l'ordre); tandis que pour le site 5, les trois meilleures techniques d'interpolation spatiale sont SK, IDW et OK, de maniere sequentielle. [Traduit par la Redaction] Mots-cles : test de penetration du cone (CPT), variabilite du site, techniques d'interpolation spatiale, analyse statistique., Introduction For analysis and design of foundations and other infrastructure, geotechnical design engineers are preferably looking for exclusive soil properties at numerous locations. However, reaching this goal is unlikely and [...]

This study examines the impact of chatbot usability on customer satisfaction in Jordanian commercial banks, focusing on the mediating role of customer empowerment. Using a quantitative approach, data was collected from bank customers in Jordan through surveys. Usability is evaluated through ease of use, responsiveness, reliability, and personalization. Customer empowerment is measured by perceived control, information accessibility, and decision-making autonomy. The findings offer insights for banks to enhance chatbot systems, improve customer satisfaction, and empower interactions. Results reveal that Reliability and Personalization significantly predict Customer Satisfaction directly and indirectly through Customer Empowerment. Ease of Use indirectly affects Satisfaction via Empowerment. Responsiveness may not significantly impact Satisfaction. These findings guide practitioners to enhance customer experiences, loyalty, and business success.

This research uses gene expression programming (GEP) in the development of a model that considers the cone penetration (CPT) data and predicts the N60 value. Some 140 CPT-SPT pairs of data points for sand, sandy, silt, and silty sand soils were used in the GEP model. The results have revealed that the GEP model can predict the standard penetration (SPT) N60 values with remarkable accuracy with high R2 values and low error for the training dataset. A comparison in terms of statistical performance between artificial neural network models (ANN) developed previously and the developed GEP model has been presented. The GEP model showed better results than the ANN model in predicting the N60 value. [ABSTRACT FROM AUTHOR]

In this study, the level of interest burden maximizing bank profits and tax revenues was assessed separately for lockdown and non-lockdown periods by using polynomial regression. According to the findings of the study, the interest burden that provides the maximum level of bank profitability for the non-lockdown period was estimated at 2.9%, and 2.0% for the lockdown period. But the interest burden that provides the maximum level of tax revenue for the non-lockdown period was estimated at 1.48%, and 1.63% for the lockdown period in Azerbaijan. Besides that, the interest burden that provides the minimum level of tax revenue for the non-lockdown period was found at 2.16%, and 1.87% for the lockdown period. In order to ensure fiscal and monetary compatibility, it is important to consider the difference between these interest burdens as far as possible within the framework of optimality. [ABSTRACT FROM AUTHOR]

Current pavement design procedures recommend the resilient modulus of subgrade materials for pavement design and analysis. The objective of this paper is to develop two statistical models to predict the resilient modulus of subgrade cohesive soils from the dynamic cone penetration (DCP) test parameters and soil properties. The first model correlates resilient modulus to the dynamic cone penetration test parameters, while the second model correlates resilient modulus to both the dynamic cone penetration test parameters and soil properties. Field and laboratory experiments were conducted at 31 sites in Louisiana that contain four common soil types (A-4, A-6, A-7-5, and A-7-6). Field tests included DCP tests and soil sampling, while laboratory tests included determining basic soil properties and resilient modulus. Statistical analyses were conducted on the collected data, and two statistical models were developed for the prediction of resilient modulus of cohesive subgrade soils from the DCP test parameters and soil properties. The models predicted a separate data set that was not used in their development, indicating the success of the application of the dynamic cone penetration test in evaluating the resilient modulus of pavement subgrade soils. The predicted values obtained from the proposed models corresponded well with the measured resilient modulus values from the repeated load triaxial test. DOI: 10.1061/(ASCE)0899-1561(2007)19:11(986) CE Database subject headings: Cone penetration; Resilience; Triaxial tests; Pavements; Subgrades.

This paper evaluates the applicability of eight direct cone penetration test (CPT) methods to predict the ultimate load capacity of square precast prestressed concrete (PPC) driven friction piles. Analyses and evaluation were conducted on 35 driven friction piles of different sizes and lengths that were failed during pile load testing. The CPT methods, as well as the static a and [beta] methods, were used to estimate the load carrying capacities of the investigated piles ([Q.sub.p]). The Butler-Hoy method was used to determine the measured load carrying capacities from pile load tests ([Q.sub.m]). The pile capacities determined using the different methods were compared with the measured pile capacities obtained from the pile load tests. Four criteria were selected as bases of evaluation: the best fit line for [Q.sub.p] versus [Q.sub.m] the arithmetic mean and standard deviation for the ratio [Q.sub.p]/[Q.sub.m], the cumulative probability for [Q.sub.p]/[Q.sub.m], and the histogram and log normal distribution for [Q.sub.p]/[Q.sub.m]. Results of the analyses showed that the best performing CPT methods are the LCPC method by Bustamante and Gianeselli as well as the De Ruiter and Beringen method. These methods were ranked number one according to the mentioned criteria. DOI: 10.1061/(ASCE)1090-0241(2004)130:9(935) CE Database subject headings: Driven piles; Pile friction; Statistical analysis; Cone penetration tests; Bearing capacity.

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 FE models for a 4 × 4 vertical, battered, and mixed PG configurations were developed and analyzed at three pile spacings (3D, 5D, 7D) and for three clay soils (soft, medium, stiff). The nonlinear pile and soil behaviors were accounted for using elastoplastic constitutive models and a pile–soil interface model. The effects of pile spacing and configuration on the group efficiency, axial reaction in piles, and p-multipliers are discussed. The results showed that the battered PG had the largest group efficiency followed by mixed PG and then vertical PG at all pile spacings. The axial reaction in piles decreased only in two pile rows when the pile spacing increased in the three PGs. The back-calculated p-multipliers were different between the PGs with the largest values in the battered PG. Modifications to the p-multipliers used in practice are proposed to account for the effect of configuration in the battered and mixed PGs. [ABSTRACT FROM AUTHOR]

Piles driven in cohesive soils usually experience a large increase in capacity over time, known as setup or freeze. This paper presents the field results of two test piles that were driven in two different locations of Louisiana to evaluate the pile setup behavior. Laboratory and in situ soil testings (cone penetration test, CPT) were conducted to characterize the subsurface soil properties. Several dynamic load tests (DLTs) and one static load test (SLT) were conducted on each test pile at different times after end of driving (EOD) to quantify the magnitude and rate of setup. The test pile in one site was instrumented with vibrating wire strain gages to calculate the load-distribution along the length of the pile during the static load test. With the aid of Case Pile Wave Analysis Program (CAPWAP®) analyses and load distribution plots from strain gages, skin friction and end-bearing capacities were measured separately. The load test results showed that the setup was mainly attributed to increase in skin friction with time and end-bearing capacity remained almost constant. The load test results also showed that setup behavior was more significant in soft cohesive soil. The pile capacity of one test pile was also evaluated using different CPT methods (such as Schmertmann, De Ruiter and Beringen, LCPC). The result showed that all the methods can estimate the pile capacity with good accuracy. [ABSTRACT FROM AUTHOR]

Background: Limited data exists to support the benefit from second-line chemotherapy in patients with metastatic urothelial carcinoma. Factors that predict survival following progression after first-line platinum-based regimens in patients treated outside clinical trials are not clear. This study intends to evaluate different prognostic factors and the impact of second-line chemotherapy on survival. Methods:We retrospectively reviewed patients with metastatic urothelial carcinoma who experienced disease progression following first-line platinum-based regimens for metastases. These patients received treatment and follow up visits at a single institution. The effect of demographic, disease characteristics, and second-line therapy on overall survival was examined through univariate and multivariate cox-regression analyses. Results: There were 64 patients included. A total of 27 (42%) patients did not receive second-line chemotherapy because of poor Eastern Cooperative Oncology Group performance status, 20 (31%) received combination chemotherapy (platinum-based in 17), and 17 (27%) received a single agent chemotherapy. The median overall survival from the date of documented progression after first-line therapy was 5.0 months. In multivariate analysis, a correlation existed between poor overall survival and performance status of ≥1 (HR: 5.74, 95% CI: 1.4- 45.57, P=0.036), no second-line chemotherapy (HR: 2.72, 95% CI: 1.39-5.31, P=0.003), and ≥2 metastatic sites (HR: 5.19, 95% CI: 1.74-15.44, P<0.001). Conclusion: A significant proportion of patients with metastatic urothelial carcinoma were not eligible for second-line chemotherapy because of poor performance status. Use of second-line chemotherapy, Eastern Cooperative Oncology Group performance status, and number of metastatic sites were important determinants of survival. [ABSTRACT FROM AUTHOR]

Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.

Six instrumented static-load test piles driven at four different locations along the LA-1 highway alignment in coastal Louisiana provided the opportunity to study the soil-setup behavior in relation to the soil properties. The instrumented piles consisted of six square prestressed-concrete (PSC) test piles of different sizes and different lengths. Both soil boring and piezocone penetration tests (PCPT) were conducted at each test-pile location to characterize the subsurface soil conditions. The testing program consisted of performing dynamic-load tests (DLTs) at predetermined time intervals, followed by one static-load test (SLT) at the end. These piles were instrumented with vibratingwire sister-bar strain gauges along their length. Case pile-wave analyses were performed on the DLT data to calculate the soil-resistance distributions along test piles. Design parameters such as the adhesion factor α and the effective stress coefficient β were also backcalculated. The α values ranged from 0.68 to 1.78, and the β values ranged from 0.11 to 0.32. The load test results showed that the shaft resistances increased significantly with time, while the toe resistances remained almost constant with time for all test piles. The rates of setup parameter A for individual soil layers were calculated using the unit shaft resistances. The resulting average A values for clayey and sandy soils were 0.36 and 0.16, respectively. The setup parameters of individual soil layers were correlated with soil properties, which showed that the A parameter decreases with increasing undrained shear strength (Su) and increases with increasing plasticity index (PI) for clayey soil layers. A nonlinear regression model based on undrained shear strength and plasticity index was developed and proven to be applicable to other sites as well. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of geosynthetic-reinforced unpaved road sections over soft subgrade using plate load test. The parameters investigated included the location and tensile modulus of geosynthetics, and the number of geosynthetic layers. Stress distribution on top of the subgrade layer and strain distribution along the geosynthetic reinforcements were also investigated. The test results indicated that geosynthetic reinforcement resulted in appreciable reduction of surface deformation and increase of bearing capacity. The test results also demonstrated the effects of geogrid arrangement/location on the performance of unpaved test sections, with double reinforcement location consistently yielded the best improvement. The definite trend of increasing Bearing Capacity Ratio (BCR) and reloading elastic modulus with increasing tensile modulus of geosynthetics was observed when geosynthetics were grouped according to aperture shape and polymer type. The BCR is defined as the ratio of the bearing capacity of reinforced unpaved sections to that of unreinforced unpaved section. [ABSTRACT FROM AUTHOR]

A finite-element response model was developed using ABAQUS software package to investigate the effect of geogrid base reinforcement on the response of a flexible pavement structure. Finite-element analyses were then conducted on different unreinforced and geogrid-reinforced flexible pavement sections. In this analysis, the base course (BC) layer was modelled using an elasto-plastic bounding surface model. The results of the finite-element analyses showed that the geogrid reinforcement reduced the lateral strains within the BC and subgrade layers, the vertical strain and shear strain at top of subgrade, and the surface permanent deformation. The higher tensile modulus geogrid resulted in larger reduction of surface permanent deformation. Based on the response parameters computed from the finite element analysis, the improvement of using geogrid for BC reinforcement was then evaluated using the damage models for rutting in the mechanistic–empirical method developed through NCHRP Project 1-37a. The results of mechanistic–empirical analyses showed that the traffic benefit ratio values can reach as high as 3.7 for thin base pavement section built over weak subgrade using high tensile modulus geogrid. [ABSTRACT FROM AUTHOR]

This paper presents reliability-based analyses for the calibration of resistance factor for axially loaded drilled shafts. A total of 16 cases of drilled-shaft load tests were collected from the Louisiana Department of Transportation and Development (LADOTD) archives. Only 11 out of the 16 cases met the Federal Highway Administration (FHwA) failure criterion. Because of the limited number of available drilled-shaft cases in Louisiana, additional 15 drilled-shaft cases were collected from a neighboring state, Mississippi, which has subsurface soil conditions similar to Louisiana soils. A database of 26 drilled shafts representing the typical design practice in Louisiana was created for a statistical reliability analysis. Predictions of load-settlement behavior of drilled shafts from soil borings were determined using the FHwA design method through the SHAFT computer program. Measured drilled-shaft axial nominal resistance was determined from either the Osterberg cell (O-cell) test or the conventional top-down static load test. Statistical analyses were performed to compare the predicted ultimate drilled-shaft nominal axial resistance and the measured nominal resistance. Results show that the selected design method underestimates the measured drilled-shaft resistance by an average of 17%. The Monte Carlo-simulation method was selected to perform the LRFD calibration under strength I limit state. Total resistance factors for different reliability indexes (β) were determined and compared with those available in literature. The LRFD calibration showed that the FHwA design method has a resistance factor ([lowercase_phi_synonym]) of 0.60 at a target reliability index (βT) of 3.0. [ABSTRACT FROM AUTHOR]

In this paper, the performance of geogrid base reinforcement in flexible pavements was evaluated using cyclic plate load testing. A 40-kN cyclic load was applied through a 305-mm diameter steel plate. The investigated parameters included geometry, location and tensile modulus of geogrids. The stress distribution, permanent vertical strain and developed porewater pressure in the subgrade, as well as, the strain distribution along geogrids were studied. The test results showed that the inclusion of geogrid can significantly improve the performance of a flexible pavement and that the traffic benefit ratio can be increased up to 15.3 at a rut depth of 19.1 mm. Better performance was observed when geogrid layer was placed at the upper one-third of the base aggregate layer. The results also demonstrated that the geogrid helps in redistributing the applied load to a wider area on top of the subgrade layer, thus resulting in less accumulated permanent deformation in the subgrade. [ABSTRACT FROM AUTHOR]

A finite-element model was developed using ABAQUS software package to investigate the effect of placing geosynthetic reinforcement within the base course layer on the response of a flexible pavement structure. A critical state two-surface constitutive model was first modified to represent the behavior of base course materials under the unsaturated field conditions. The modified model was then implemented into ABAQUS through a user defined subroutine, UMAT. The implemented model was validated using the results of laboratory triaxial tests. Finite-element analyses were then conducted on different unreinforced and geosynthetic reinforced flexible pavement sections. The results of this study demonstrated the ability of the modified critical state two-surface constitutive model to predict, with good accuracy, the response of the considered base course material at its optimum field conditions when subjected to cyclic as well as static loads. The results of the finite-element analyses showed that the geosynthetic reinforcement reduced the lateral strains within the base course and subgrade layers. Furthermore, the inclusion of the geosynthetic layer resulted in a significant reduction in the vertical and shear strains at the top of the subgrade layer. The improvement of the geosynthetic layer was found to be more pronounced in the development of the plastic strains rather than the resilient strains. The reinforcement benefits were enhanced as its elastic modulus increased. [ABSTRACT FROM AUTHOR]

Field and laboratory testing programs were conducted to develop resilient modulus prediction models for application in the design and evaluation procedures of pavement structures. The field testing program included conducting several in-situ tests such as Geogauge, Light Falling Weight Deflectometer, and Dynamic Cone Penetrometer (DCP). The laboratory program consisted of performing repeated load triaxial resilient modulus tests, physical properties, and compaction tests on soil samples obtained from tested sections. A total of four subgrade soil types at different moisture-dry unit weight levels were considered. Comprehensive statistical analyses were conducted on the field and laboratory test results. Two sets of models were developed. The first set (direct model) directly relates the laboratory measured resilient modulus values with the results of each of the three in-situ devices, whereas the second set (soil property model) incorporates soil properties in addition to the results of each of the three in-situ devices. A good agreement was observed between the predicted and measured values of the resilient modulus. Furthermore, the results showed that the resilient modulus prediction was enhanced when the soil properties were included as variables within the models. Among the models developed, the DCP-soil property model had the best prediction of resilient modulus followed by the DCP-direct model. The effectiveness of the DCP models were further evaluated during a forensic analysis of pavement section failure in a highway within Louisiana. [ABSTRACT FROM AUTHOR]