Research Article
The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications
Yasser Osman El Gammal*
Issue:
Volume 9, Issue 5, October 2024
Pages:
83-105
Received:
19 August 2024
Accepted:
7 September 2024
Published:
10 October 2024
DOI:
10.11648/j.eas.20240905.11
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Abstract: The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed "Architectural Design Process-Cognitive Pilot Map" from an architectural perspective to aid AI programmers in developing architecture design software applications.
Abstract: The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations witho...
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Research Article
Using Machine Learning Techniques to Predict Significant Wave Height Compared with Parametric Methods
Hassan Salah*,
Mohamed Elbessa
Issue:
Volume 9, Issue 5, October 2024
Pages:
106-128
Received:
10 September 2024
Accepted:
27 September 2024
Published:
18 October 2024
DOI:
10.11648/j.eas.20240905.12
Downloads:
Views:
Abstract: Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzzy Inference Systems, Genetic Algorithm, Machine Learning, etc. have been used to predict sea wave parameters in many marine areas around the world. The ease of application, high accuracy and low computational time of these techniques make them a very good choice in many engineering applications. This study focuses on prediction of significant wave height (Hs) by applying one of the most advanced Machine Learning techniques known as Support Vector Machine (SVM). SVM models are built on the basis of different Kernel functions (Linear, Sigmoid, Radial Basis Function, and Polynomial) which transform the input data into an n-dimensional space where a hyperplane can be generated to partition the data. The results of SVM models are analyzed, evaluated and then compared with the results of commonly used parametric models (P-M, SPM, and CEM). This study shows that the P-M model has reliable and satisfactory results among all parametric models, as its statistical errors are close to those of SVM models (RBF and Polynomial), while all of them are identical in their correlation factors (0.999). Moreover, the parametric models (SPM and CEM) are more accurate in their results than the SVM models (Linear and Sigmoid). Also, this study confirms that the SVM models (RBF and polynomial) are the most accurate models overall, as they have the best generalization error among all models. Finally, it can be concluded that SVM models (RBF and Polynomial) are a promising technique in the sea wave height prediction and can be used as an economic and accurate alternative solution to other prediction models.
Abstract: Prediction of Sea Wave parameters is an important issue as it is the main design factor for maritime structures. Previously, researchers have used many parametric and numerical approaches, which may be complex in application, take a long time in preparation and sometimes require a bathymetric survey. Recently, soft computing techniques such as Fuzz...
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