AI in the field of Structural Engineering has proved to be more efficient:
- Software and devices with intelligence matching that of people are developed using Artificial Intelligence (AI).
- Comparing to traditional modelling methodologies, AI is proven as an effective replacement strategy.
- AI has advantages over conventional approaches for dealing with issues involving uncertainty and is a useful tool for resolving such challenging issues.
- Additionally, when defining design engineering parameters, solutions based by AI are efficient replacements for testing, which saves the time and also effort needed by people for trials.
- AI can also increase computer efficiency, decrease mistake rates, and speed up decision-making.
- The latest intelligent techniques in structural engineering that have recently gained a lot of interest among the many AI techniques are machine learning(ML), pattern recognition(PR) and deep learning(DL).
- Machine-Learning (ML) is a subfield of Artificial-Intelligence (AI) that focuses on the creation of algorithms and models that allow computers or machines to learn from data and make predictions or judgements without being explicitly programmed. Instead of relying solely on explicit instructions, it entails developing systems which can automatically learn from and improve upon examples or experience.
- Pattern recognition (PR) refers to the cognitive process of identifying and interpreting patterns within data or sensory input. It involves the ability to recognize and make sense of regularities, similarities, and relationships among various elements or features. Overall, pattern recognition is an essential cognitive ability that humans possess and an area of study that aims to develop algorithms and models to mimic this ability in machines. By recognizing patterns, we can gain insights, make predictions, and derive meaning from complex data.
- Deep learning (DL) is sub-field of ML that focuses on development and application of artificial-neural networks with multiple layers, known as deep-neural networks. It is inspired by structure and function of human brain, particularly the interconnected network of neurons.
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