Complexity

Technologies-Based Advanced Machine Learning Models: Applications in Civil Engineering 2021


Publishing date
01 Dec 2022
Status
Closed
Submission deadline
15 Jul 2022

1Duy Tan University, Da Nang, Vietnam

2The British University in Dubai, Dubai, UAE

3Clemson University, Clemson, USA

This issue is now closed for submissions.
More articles will be published in the near future.

Technologies-Based Advanced Machine Learning Models: Applications in Civil Engineering 2021

This issue is now closed for submissions.
More articles will be published in the near future.

Description

The existence of uncertainty, nonlinearity, and nonstationary characteristics in critical civil engineering problems have necessitated the analysis of nonlinear systems with stochastic parameters, input, and boundary conditions. Stochastic methodologies present a rational basis for system analysis and sustainable design. In addition, solving stochastic dynamics is the main interest of multidisciplinary engineers and scientists. Subsequently, the advancement of the utilization of theoretical research has been an essential motivation toward simulating the stochastic behavior of complex systems, prediction, and nonlinear dynamic phenomena. Implementing and adopting new theoretical methodologies can offer a robust and reliable tool for diverse engineering applications.

With the great development of modern computer aid and computational models, computational science and engineering have accomplished massive success over the past two decades. However, there are still several limitations in civil engineering problems using computational science methodologies. The field of machine learning “artificial intelligence (AI)” was launched in 1956. However, it has been only in the last decade that significant progress has been made to allow the technology to be widely used and experienced by many outside technology circles. Today, AI is one of the fastest-growing emerging technologies and describes machines that can perform tasks that previously required human intelligence. Although noticeable progress has been achieved to date in the domain of civil engineering applications, the exploration of new robust machine learning models is still in progress and several scientists are establishing a new era in this domain for solving complex problems.

This Special Issue invites researchers and scientists to contribute by submitting their related research on the practicability of AI technologies for solving civil engineering complex problems. The focus of civil engineering applications shall be related to structural, geotechnical, material, construction management, hydraulic, and environmental problems. Submissions to this Special Issue must be concentrated on solving complex civil engineering problems that can facilitate new solutions and technologies for diverse civil engineering domains. The submissions shall also cover the new prospective of soft computing topic such as optimization, prediction, tools, analysis, measurement, and theoretical applications.

Potential topics include but are not limited to the following:

  • Advanced artificial intelligence applications
  • Non-linear and nonstationary simulation
  • Solving civil engineering problems
  • Data mining and decision analytics
  • Modeling stochastic problems
  • Optimization and analysis
  • Construction management engineering
  • Simulation of environmental problems
  • Solving complexity dynamic problems
  • Deep learning models applications
  • Machine learning and data analytics
  • Statistical methodologies in materials science
  • Mechanical process and system dynamics

Articles

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  • - Review Article

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

Alam Zeb | Fakhrud Din | ... | Kamal Z. Zamli
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  • - Volume 2023
  • - Article ID 7953967
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Fluid Flow Behavior Prediction in Naturally Fractured Reservoirs Using Machine Learning Models

Mustafa Mudhafar Shawkat | Abdul Rahim Bin Risal | ... | Ahmed W. Al Zand
  • Special Issue
  • - Volume 2022
  • - Article ID 4285328
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation

Zainab Hasan Ali | Abbas M. Burhan | ... | Zainab Al-Khafaji
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  • - Volume 2022
  • - Article ID 4998200
  • - Research Article

Application of Extreme Learning Machine Algorithm for Drought Forecasting

Muhammad Ahmad Raza | Mohammed M. A. Almazah | ... | Fuad S. Al-Duais
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  • - Article ID 4981539
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Design of System-of-System Acquisition Analysis Using Machine Learning

Fahad H. Alshammari
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  • - Volume 2022
  • - Article ID 4815623
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Wind Effects on Rectangular and Triaxial Symmetrical Tall Building Having Equal Area and Height

Astha Verma | Rahul Kumar Meena | ... | S. Anbukumar
  • Special Issue
  • - Volume 2022
  • - Article ID 6532763
  • - Research Article

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

Mohammed Majeed Hameed | Faidhalrahman Khaleel | ... | Mohammed Abdulhakim AlSaadi
  • Special Issue
  • - Volume 2022
  • - Article ID 1172805
  • - Research Article

Characterization of Meteorological Drought Using Monte Carlo Feature Selection and Steady-State Probabilities

Rizwan Niaz | Fahad Tanveer | ... | A.Y. Al-Razami
  • Special Issue
  • - Volume 2022
  • - Article ID 5433474
  • - Research Article

Compressive Strength Prediction Using Coupled Deep Learning Model with Extreme Gradient Boosting Algorithm: Environmentally Friendly Concrete Incorporating Recycled Aggregate

Mayadah W. Falah | Sadaam Hadee Hussein | ... | Ahmed A. Ewees
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.