Kundan Meshram – författare

Machine Learning Applications in Civil Engineering discusses machine learning and deep learning models for different civil engineering applications. These models work for stochastic methods wherein internal processing is done using randomized prototypes. The book explains various machine learning model designs that will assist researchers to design multi domain systems with maximum efficiency. It introduces Machine Learning and its applications to different Civil Engineering tasks, including Basic Machine Learning Models for data pre-processing, models for data representation, classification models for Civil Engineering Applications, Bioinspired Computing models for Civil Engineering, and their case studies.

Using this book, civil engineering students and researchers can deep dive into Machine Learning, and identify various solutions to practical Civil Engineering tasks.

This exciting new volume covers the most up-to-date advances, theories, and practical applications for non-motorized transportation (NMT) systems, geographic information system-based transportation systems, and signal processing for urban transportation systems.

This book will allow readers to readers to identify traffic and transport problems in cities and to study mass transportation systems, and modes of transportation and their characteristics, focusing on transportation infrastructure which includes green bays, control stations, mitigation buildings, separator lanes, and safety islands. From this, readers will be able to study urban public transport systems and gain some background into intelligent transportation and telemetric systems, including techniques for designing transport telemetric systems and applying them to urban transportation. Applications include advanced traffic management systems, advanced traveler information systems, advanced vehicle control systems, commercial vehicle operational management, advanced public transportation systems, electronic payment systems, advanced urban transportation, security and safety systems, and urban traffic control.

From this, an artificial Intelligence-based transportation system design using genetic algorithms and neural networks is discussed, to show applications in designs. These models and their studies are further extended in signal processing systems and geographic information systems (GIS) to improve transportation system design, and to apply this to the design of non-motorized transportation models, while ensuring pedestrian safety. All these models are further analyzed for environmental impact assessment, which include structural audits, analysis of site selection procedure, baseline conditions and major concerns, green building and its advantages, the description of potential environmental effects, and many more interesting topics.

This exciting new volume covers the most up-to-date advances, theories, and practical applications for non-motorized transportation (NMT) systems, geographic information system-based transportation systems, and signal processing for urban transportation systems.

This book will allow readers to readers to identify traffic and transport problems in cities and to study mass transportation systems, and modes of transportation and their characteristics, focusing on transportation infrastructure which includes green bays, control stations, mitigation buildings, separator lanes, and safety islands. From this, readers will be able to study urban public transport systems and gain some background into intelligent transportation and telemetric systems, including techniques for designing transport telemetric systems and applying them to urban transportation. Applications include advanced traffic management systems, advanced traveler information systems, advanced vehicle control systems, commercial vehicle operational management, advanced public transportation systems, electronic payment systems, advanced urban transportation, security and safety systems, and urban traffic control.

From this, an artificial Intelligence-based transportation system design using genetic algorithms and neural networks is discussed, to show applications in designs. These models and their studies are further extended in signal processing systems and geographic information systems (GIS) to improve transportation system design, and to apply this to the design of non-motorized transportation models, while ensuring pedestrian safety. All these models are further analyzed for environmental impact assessment, which include structural audits, analysis of site selection procedure, baseline conditions and major concerns, green building and its advantages, the description of potential environmental effects, and many more interesting topics.