## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.1 Architectural Compositions, Building Technologies and Materials  
The domain of architectural compositions, building technologies and materials covers architectural designs of building units or ensembles; construction art and systems and methods of construction works; the technology of construction materials; structural physics; and microclimate control and maintenance and restoration of old buildings and monuments.  
The field in question can benefit significantly from using physics-based artificial intelligence models and implementing analytical differential equations or other mathematical models used to solve structural physics problems or simulations [13]. Concrete is the most widely used construction material, but it is also a recognized pollutant that causes significant sustainability issues in terms of resource depletion, energy use and greenhouse gas emissions. AI can lessen the environmental impact of concrete to increase its long-term sustainability. For example, the authors of [36] developed a model to forecast the compressive strength of various eco-friendly concrete mixtures, which may be used in the design process. A combination of recycled concrete and blast furnace slag is utilized to create the concrete. As a final step, a machine learning model was developed that accurately predicts the compressive strength of green concrete.  
Self-healing concepts have not yet produced design solutions that reliably quantify their positive effects on structural performance, despite a vast body of literature [37]. Concrete and other cement-based materials have an inherent ability to self-heal [38]. It has been demonstrated that the effectiveness of concrete's self-strengthening and self-healing depends on several factors, the most significant of which are the type of exposure, the width of the crack and the presence of healing stimulants such as crystalline impurities [39]. Autogenous self-healing is largely unaffected by additional factors such as fiber count and additional cementitious materials. A related study [40] proposed, through a properly constructed neural network design and analysis diagrams, a simple input-output model for rapid prediction and evaluation of the self-healing effectiveness of cement-based materials. In particular, it uses AI techniques to quantify the performance of material recovery by displaying the quantitative correlations between mix ratios, exposure type and duration and initial crack width. Specifically, for ANN analysis, a back-propagation network was developed in this study. A single-layer feed-forward neural network was developed. In this network, the output and hidden layers are associated with biases. The training of data is performed by the method of supervised learning in this algorithm. The predicted output values are compared with the provided outputs and the associated error is calculated. The weights and biases of each neural network are adjusted based on this error. This process is iteratively carried out until the error is below the desired value or the maximum number of iterations is reached. In terms of assessing structural performance deterioration and significantly extending the life of reinforced concrete structures, this is the first systematic incorporation of self-healing principles into durability-based design methodologies.