Explore the vast range of titles available.

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB ® exercises 1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work Paresh Chandra Deka earned a bachelor’s in civil engineering at the National Institute of Technology, Silchar, Assam, India, and a PhD at the Indian Institute of Technology, Guwahati, specializing in hydrological modeling. Dr. Deka served on the faculty at the School of Postgraduate Studies at Arbaminch University, Ethiopia from 2005 to 2008 and as visiting faculty in 2012 at the Asian Institute of Technology, Bangkok, Thailand. He has supervised 10 PhD scholars as well as 5 current PhD scholars. He has supervised 40 master’s theses as well as 4 current master’s students. His research area is soft computing applications in water resources engineering and management. Dr. Deka has published 4 books, 5 book chapters, and more than 40 international journal papers. He is a visiting faculty member doing short-term research interaction at Purdue University, Indiana. With more than 28 years of teaching experience, he is currently a professor in the Department of Applied Mechanics and Hydraulics at the National Institute of Technology, Surathkal, Karnataka, India.

This book introduces the methods for predicting the future behavior of a system's health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:- Prognostics tutorials using least-squares- Bayesian inference and parameter estimation- Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter- Data-driven prognostics algorithms including Gaussian process regression and neural network- Comparison of different prognostics algorithms The authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low-cost structures has become crucially important in modern engineering design.

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.

In this paper, an investigation of the temporal rainfall variability, in a large area of southern Italy, has been carried out using a homogeneous monthly rainfall dataset of 559 rain gauges with more than 50 years of observation. The area under investigation is a large portion of the Italian peninsula, ranging from the Campania and the Apulia regions in the North, to Sicily in the South, and covering an area of about 85,000 km2. Possible trends in seasonal and annual rainfall values have been detected by means of a new graphical technique, Şen’s method, which allows the trend identification of the low, medium and high values of a series. Moreover, the Mann–Kendall test has been also applied. As a result, different values and tendencies of the highest and of the lowest rainfall data have emerged among the five regions considered in the analysis. In particular, at seasonal scale, a negative trend has been detected especially in winter and in autumn in the whole study area, whereas not well defined trend signals have been identified in summer and spring.

Machine learning (ML)-based techniques have found significant impact in many fields of engineering and sciences, where data-sets are available from experiments and high-fidelity numerical simulations. Those data-sets are generally utilised in a machine learning model to extract information about the underlying physics and derive functional relationships mapping input variables to target quantities of interest. Commonplace machine learning algorithms utilised in scientific machine learning (SciML) include neural networks, support vector machines, regression trees, random forests, etc. The focus of this article is to review the applications of ML in naval architecture, ocean and marine engineering problems; and identify priority directions of research. We discuss the applications of machine learning algorithms for different problems such as wave height prediction, calculation of wind loads on ships, damage detection of offshore platforms, calculation of ship-added resistance and various other applications in coastal and marine environments. The details of the data-sets including the source of data-sets utilised in the ML model development are included. The features used as the inputs to the ML models are presented in detail and finally, the methods employed in optimisation of the ML models were also discussed. Based on this comprehensive analysis, we point out future directions of research that may be fruitful for the application of ML to ocean and marine engineering problems.

Application of the image processing techniques (IPT) to identify rock mass geometry provides more fast information about discontinuity properties used in geo-engineering characteristics. In this regard, the field survey can be improved using IPT. This study has utilised the IPT to identify the discontinuity and block volume characteristics in a discontinuous rock mass. For this purpose, a visual evaluation of the rock mass outcrop with discontinuities from a road slope cut located in the South Pars Special Zone, Assalouyeh, Iran, was considered. A three-step IPT analysis (i.e. pre-processing, main processing, and post-processing) was conducted to extract the features through the Python programming language. Regarding the IPT methodology, the studied rock mass characteristics consist of four major discontinuity sets and rock block volumes between the intersections of the discontinuities, as confirmed with a scan-line field survey. The evaluated data indicated that the maximum, minimum, and average block volumes processed by the IPT were 1.068, 0.479, and 1.055 m3, and their field measurement results were 1.092, 0.479, and 1.065 m3, respectively. Additionally, the orientations of the estimated discontinuity properties and their spacings determined by IPT for the rock mass ranging between 32 and 69.9° and 0.5 and 2.18 m, respectively. Similarly, the orientations of the field measurement results were also obtained between 33 and 71° and 0.58 and 2.25 m, respectively. The results of the IPT and the field survey were close, which revealed that the IPT is a reliable method for determining discontinuity spacing and rock block volume along large cut slopes. This approach provided rapid data processing with spatial extensions in a short period, making it possible to achieve accurate results in discontinuity network characteristics.

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.