Engineering Commons^™

Research And Comparison Of Pavement Performance Prediction Based On Neural Networks And Fusion Transformer Architecture, Hui Yao, Ke Han, Yanhao Liu, Dawei Wang, Zhanping You

Michigan Tech Publications, Part 2

The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior …

Inversion Iterative Correction Method For Estimating Shear Strength Of Rock And Soil Mass In Slope Engineering, Wei Jiang, Ye Ouyang, Jin-Zhou Yan, Zhi-Jian Wang, Li-Peng Liu

Rock and Soil Mechanics

For the slopes that have failed or deformed significantly, the shear strength of rock and soil mass is frequently inversely estimated based on a factor of safety assumed. For the slope with a sliding surface passing through multi-layer rock and soil mass, it is unreasonable to achieve this goal by blind trial. To solve this issue, back propagation (BP) neural network is constructed using shear strength of multi-layer rock and soil mass as the input, and the factor of safety of slope, the entrance and exit positions of the sliding surface obtained by Geoslope as the outputs. Then, based on …

Crash Injury Severity Prediction With Artificial Neural Networks, Rima Abisaad

Dissertations

Motor vehicle crashes are one of our nation's most serious social, economic and health issues. They are the leading cause of death among children and young adults, killing approximately 1.35 million people each year. Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. To help reduce traffic fatalities and injuries on roadways, crash prediction models are used to forecast the injury severity of potential crashes and apply precautionary countermeasures accordingly. Most of these models are reactive as they use historical crash data to categorize crash-related factors. Recently, advancements have been made in developing …

Construction Labor Productivity Modeling And Use Of Neural Networks: A Bibliometric Survey, Shalaka Hire, Sayali Sandbhor

Library Philosophy and Practice (e-journal)

Productivity of a project has a major impact on its cost and profitability. In spite of construction being labor intensive field with labor cost adding up to 30% to 50% of overall project cost, the productivity of labor is one of the least studied areas in the construction industry. It requires to be given due attention to the issues affecting labor productivity and design solution using soft computing techniques to improve the overall performance of the industry. This research paper aims to conduct a bibliographic survey of the literature available in the domain of Labor Productivity (LP) as well as …

Comparison Of Deep Convolutional Neural Networks And Edge Detectors For Image-Based Crack Detection In Concrete, Sattar Dorafshan, Robert J. Thomas, Marc Maguire

Civil and Environmental Engineering Faculty Publications

This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they …

Fuzzy Vs Neural Network Models For Environmental Decision Support System Implementation Aiming To Standardise The Multiparametric Decision In A Drinking Water Plant With Electrodialysis Reversal, Montse Dalmau, Hèctor Monclús, Joan Espasa, Natasa Atanasova, Manel Poch, Pere Emiliano, Oriol Capdevila, Santiago González, Fernando Valero

International Congress on Environmental Modelling and Software

The development of an environmental decision support system (EDSS) by means of two different aims to support the operators’ decisions in the drinking water treatment plant (DWTP), equipped with the biggest electrodialysis reversal (EDR) in the world has been tested. A fuzzy artificial neural network model (fuzzy) and an artificial neural network (ANN) have been compared for optimizing the decision: how to manage water blending ratios from EDR and conventional treatment of the drinking water plant, evaluating Llobregat River characteristics (inlet DWTP), current operating conditions of DWTP, weather conditions and distribution requirements on-line. This tool has been tested among 4,5 …

Driver Engagement In Secondary Tasks: Behavioral Analysis And Crash Risk Assessment, Mengqiu Ye

LSU Master's Theses

Distracted driving has long been acknowledged as one of the leading causes of death or injury in roadway crashes. The focus of past research has been mainly on the change in driving performance due to distracted driving. However, only a few studies attempted to predict the type of distraction based on driving performance measures. In addition, past studies have proven that driving performance is influenced by the drivers’ socioeconomic characteristics, while not many studies have attempted to quantify that influence. In essence, this study utilizes the rich SHRP 2 Naturalistic Driving Study (NDS) database to (a) develop a model for …

Recognition System Of Indonesia Sign Language Based On Sensor And Artificial Neural Network, Endang Supriyati, Mohammad Iqbal

Makara Journal of Technology

Sign language as a kind of gestures is one of the most natural ways of communication for most people in deaf community. The aim of the sign language recognition is to provide a translation for sign gestures into meaningful text or speech so that communication between deaf and hearing society can easily be made. In this research, the Indonesian sign language recognition system based on flex sensors and an accelerometer is developed. This recognition system uses a sensory glove to capture data. The sensor data that are processed into feature vector are the 5-fingers bending and the palm acceleration when …

Statistical And Numerical Integrated Approach For Detecting Onset Of Prefabricated Bridge Component Connection Deterioration, Cem Mansiz

Masters Theses

Bridges are the substantial part of the transportation infrastructure. Most recent report shows that of the 605,086 bridges in the United States, 67,526 (11%) are deemed structurally deficient, and 76,363 (13%) are declared functionally obsolete (FHWA, 2011). Deck is the shelter of a bridge that is subjected to severe loads due to exposure and traffic. Importance of detecting deck deterioration is further highlighted with the introduction of accelerated bridge construction (ABC) where prefabricated components are brought to the site, assembled, and connected using field cast joints. However, durability performance of field cast connections is not encouraging. Hence, continuous monitoring of …

Effect Of Land Cover Heterogeneity On Soil Moisture Retrieval Using Active Microwave Remote Sensing Data, Tarendra Lakhankar, Hosni Ghedira, Marouane Temimi, Amir E. Azar, Reza Khanbilvardi

Publications and Research

This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of subgrid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Backpropagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number …

Subsurface Characterization Using Textural Features Extracted From Gpr Data, R. S. Freeland, Lameck O. Odhiambo

Department of Biological Systems Engineering: Papers and Publications

Subsurface conditions can be non-intrusively mapped by observing and grouping patterns of similarity within ground-penetrating radar (GPR) profiles. We have observed that the intricate and often visually indiscernible textural variability found within a complex GPR image possesses important parameters that help delineate regions of similar subsurface characteristics. In this study, we therefore examined the feasibility of using textural features extracted from GPR data to automate subsurface characterization. The textural features were matched to a “fingerprint” database of previous subsurface classifications of GPR textural features and the corresponding physical probings of subsurface conditions. Four textural features (energy, contrast, entropy, and homogeneity) …

Neural Networks For Estimating The Productivity Of Brick Works And Plastering Works In Egypt, Mohamed Aly Masoud

Archived Theses and Dissertations

Neural networks have proven apparent success in solving a wide variety of problems. Various engineering applications in particular were studied using these powerful tools.

The Neural network resembles the function of the human brain. A training

process has to be applied first in order that the network acquires knowledge through learning. Then the network can generalize predicted solutions based on the a mount and accuracy of the training data. The target of this research is to develop two neural networks that can predict the productivity of two common construction-finishing activities, which are brick and plaster works. In order to adequately …

Optimal Design Of Aquifer Clean Up Systems Under Uncertainty Using A Neural Network And A Genetic Algorithm, Alaa H. Aly, Richard C. Peralta

Civil and Environmental Engineering Faculty Publications

We present a methodology to account for the stochastic nature of hydraulic conductivity during the design of pump-and-treat systems for aquifer cleanup. The methodology (1) uses a genetic algorithm to find the global optimal solution and (2) incorporates a neural network to model the response surface within the genetic algorithm. We apply the methodology for a real example and different optimization scenarios. The employed optimization formulation requires few hydraulic conductivity realizations. The presented approach produces a trade-off curve between reliability and treatment facility size .