Titlebook: New Advances in Soft Computing in Civil Engineering; AI-Based Optimizatio Gebrail Bekda?,Sinan Melih Nigdeli Book 2024 The Editor(s) (if ap

易怒

Hemiplegia

Machine Learning Approaches for Predicting Compressive and Shear Strength of EB FRP-Reinforced Concr enhancing structural performance, durability, and service life, yet accurately predicting the shear strength of these elements remains complex due to intricate interactions between influencing factors that conventional empirical equations struggle to capture. This comprehensive review critically a

EVICT

教育學(xué)

Prediction of Bi-Linear Strength Envelope of Brazilian Soils Using Machine Learning Techniques,r, based on recently produced studies, it is believed that the development of computational models to estimate them is a tool capable of meeting this demand. This study aims, therefore, to develop a machine learning model capable of estimating bi-linear strength envelopes of soils. In order to achie

monochromatic

Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tool method of determining the UCS is often expensive and time-consuming. Also, the determination of UCS by conventional methods is less accurate and reliable because of the maintenance and calibration of instruments. Therefore, many empirical and advanced computational methods have been introduced and

jet-lag

A Review of Deformations Prediction for Oil and Gas Pipelines Using Machine and Deep Learning,preserving pipeline integrity is important for a secure and sustainable energy provider. The fast development of Machine Learning (ML) methods gives a beneficial possibility to build predictive models that can efficiently resolve these complex problems. This review paper principally emphasizes apply

esculent

,Determination of the Effect of XGBoost’s Parameters on a Structural Problem, walls have both structural constraints and constraints such as overturning, shear and soil-bearing capacity. In this chapter, a dataset is generated by optimizing the cantilever-type reinforced concrete retaining wall with Teaching Learning Based Optimization (TLBO). This dataset is analyzed with E

Constrain

Vulvodynia

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