Probe People and Vehicle-Based Data Sources Application in Smart Transportation

Smart transportation is a framework that leverages the power of Information and Communication Technology for acquisition, management, and mining of traffic-related data sources. This chapter categorizes them into probe people and vehicles based on Global Positioning Systems, mobile phone cellular networks, and Bluetooth, location-based social networks, and transit data with the focus on smart cards. For each data source, the operational mechanism of the technology for capturing the data is succinctly demonstrated. Secondly, as the most salient feature of this study, the transport-domain applications of each data source that have been conducted by the previous studies are reviewed and classified into the main groups. Possible research directions are provided for all types of data sources. Finally, authors briefly mention challenges and their corresponding solutions in smart transportation.

A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service which is also known as bending over sheave fatigue lifetime. It helps to take precaution in the plant in advance and eliminate catastrophic accidents due to usage of rope when allowable bending cycles are exceeded. To determine bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and longer time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.

Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

Application of Machine Learning Methods for Passenger Demand Prediction in Transfer Stations of Istanbul's Public Transportation System

The rapid growth in the number of drivers and vehicles in the population and the need for easy transportation of people increases the importance of public transportation. Traffic becomes a growing problem in Istanbul which is Turkey's greatest urban settlement area. Decisions on investments and projections for the public transportation should be well planned by considering the total number of passengers and the variations in the demand on the different regions. The success of this planning is directly related to the accurate passenger demand forecasting. In this study, machine learning algorithms are tested in a real-world demand forecasting problem where hourly passenger demands collected from two transfer stations of a public transportation system. The machine learning techniques are run in the WEKA software and the performance of methods are compared by MAE and RMSE statistical measures. The results show that the bagging based decision tree methods and rules methods have the best performance.

Analysis of Ground Water Quality Using Statistical Techniques

Urbanization, industrialization, and increase in population lead to depletion of ground water quantity and also deteriorate the ground water quality. Madurai city is one of the oldest cities in India. In this chapter the ground water quality was assessed using various statistical techniques. Groundwater samples were collected from 11 bore wells and 5 dug wells in Post-monsoon season during 2002. Samples were analysed for physico-chemical characterization in the laboratory. Around 17 physico-chemical parameters were analysed for all the samples. The descriptive statistical analysis was done to understand the correlation between each parameter. Cluster Analysis was carried out to identify the most affected bore well and dug well in the Madurai city.

Artificial Intelligence Towards Water Conservation

Innovation and technology are trending in the industry 4.0 revolution, and dealing with environmental issues is no exception. The articulation of artificial intelligence (AI) and its application to the green economy, climate change, and sustainable development is becoming mainstream. Water as a resource is one of them which has direct and indirect interconnectedness with climate change, development, and sustainability goals. In recent decades, several national and international studies revealed the application of AI and algorithm-based studies for integrated water management resources and decision-making systems. This chapter identifies major approaches used for water conservation and management. On the basis of a literature review, authors will outline types of approaches implemented through the years and offer instances of the ways different approaches selected for water conservation and management studies are relevant to the context.

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Optimum Design of Carbon Fiber-Reinforced Polymer (CFRP) Beams for Shear Capacity via Machine Learning Methods

In this chapter, an application for demonstrating prediction success and error performance of ensemble methods combined via various machine learning and artificial intelligence algorithms and techniques was performed. For this reason, two single method was selected and combination models with Bagging ensemble was constructed and operated in the direction optimum design of concrete beams covering with carbon fiber reinforced polymers (CFRP) by ensuring the determination of design variables. The first part was optimization problem and method composing from an advanced bio-inspired metaheuristic called Jaya algorithm. Machine learning prediction methods and their operation logics were detailed. Performance evaluations and error indicators were represented for prediction models. In the last part, performed prediction applications and created models were introduced. Also, obtained prediction success for main model generated with optimization results was utilized to determine the optimum predictions about test models.

High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods

In this chapter, prediction applications of concrete compressive strength values were realized via generation of various hybrid models, which are based on decision trees as main prediction method, by using different artificial intelligence and machine learning techniques. In respect to this aim, a literature research was presented. Used machine learning methods were explained together with their developments and structural features. Various applications were performed to predict concrete compressive strength, and then feature selection was applied to prediction model in order to determine primarily important parameters for compressive strength prediction model. Success of both models was evaluated with respect to correct and precision prediction of values with different error metrics and calculations.