Pavement performance prediction using adaptive logic networks

Pavement performance is one of the most important components of the pavement management system. Prediction of the future performance of a pavement section is important in programming maintenance and rehabilitation needs. Models for predicting pavement performance have been developed on the basis of traffic and age. The purpose of this research is to extend the use of a relatively new approach to performance prediction in pavement performance modeling using adaptive logic networks (ALN). Adaptive logic networks have recently emerged as an effective alternative to artificial neural networks for machine learning tasks. ^ The ALN predictive methodology is applicable to a wide variety of contexts including prediction of roughness based indices, composite rating indices and/or individual pavement distresses. The ALN program requires key information about a pavement section, including the current distress indexes, pavement age, climate region, traffic and other variables to predict yearly performance values into the future. ^ This research investigates the effect of different learning rates of the ALN in pavement performance modeling. It can be used at both the network and project level for predicting the long term performance of a road network. Results indicate that the ALN approach is well suited for pavement performance prediction modeling and shows a significant improvement over the results obtained from other artificial intelligence approaches. ^

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Controlled growth of carbon nanotubes for high performance nanoelectronics

Unique electrical and mechanical properties of single-walled carbon nanotubes (SWNTs) have made them one of the most promising candidates for next-generation nanoelectronics. Efficient utilization of the exceptional properties of SWNTs requires controlling their growth direction (e.g., vertical, horizontal) and morphologies (e.g., straight, junction, coiled). ^ In this dissertation, the catalytic effect on the branching of SWNTs, Y-shaped SWNTs (Y-SWNTs), was investigated. The formation of Y-shaped branches was found to be dependent on the composition of the catalysts. Easier carbide formers have a strong tendency to attach to the sidewall of SWNTs and thus enhance the degree of branching. Y-SWNTs based field-effect transistors (FETs) were fabricated and modulated by the metallic branch of the Y-SWNTs, exhibiting ambipolar characteristics at room temperature. A subthreshold swing of 700 mV/decade and an on/off ratio of 105 with a low off-state current of 10-13 A were obtained. The transport phenomena associated with Y- and cross-junction configurations reveals that the conduction mechanism in the SWNT junctions is governed by thermionic emission at T > 100 K and by tunneling at T < 100 K. ^ Furthermore, horizontally aligned SWNTs were synthesized by the controlled modification of external fields and forces. High performance carbon nanotube FETs and logic circuit were demonstrated utilizing the aligned SWNTs. It is found that the hysteresis in CNTFETs can be eliminated by removing absorbed water molecules on the CNT/SiO2 interface by vacuum annealing, hydrophobic surface treatment, and surface passivation. SWNT “serpentines” were synthesized by utilization of the interaction between drag force from gas flow and Van der Waals force with substrates. The curvature of bent SWNTs could be tailored by adjusting the gas flow rate, and changing the gas flow direction with respect to the step-edges on a single-crystal quartz substrate. Resistivity of bent SWNTs was observed to increase with curvature, which can be attributed to local deformations and possible chirality shift at curved part. ^ Our results show the successful synthesis of SWNTs having controllable morphologies and directionality. The capability of tailoring the electrical properties of SWNTs makes it possible to build an all-nanotube device by integrating SWNTs, having different functionalities, into complex circuits. ^