Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In the (...) second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

The application of recycled aggregate as a sustainable material in construction projects is considered a promising approach to decrease the carbon footprint of concrete structures. Prediction of compressive strength of environmentally friendly concrete containing recycled aggregate is important for understanding sustainable structures’ concrete behaviour. In this research, the capability of the deep learning neural network approach is examined on the simulation of CS of EF concrete. The developed approach is compared to the well-known artificial intelligence approaches named multivariate adaptive regression (...) spline, extreme learning machines, and random forests. The dataset was divided into three scenarios 70%-30%, 80%-20%, and 90%-10% for training/testing to explore the impact of data division percentage on the capacity of the developed AI model. Extreme gradient boosting was integrated with the developed AI models to select the influencing variables on the CS prediction. Several statistical measures and graphical methods were generated to evaluate the efficiency of the presented models. In this regard, the results confirmed that the DLNN model attained the highest value of prediction performance with minimal root mean squared error. The study revealed that the highest prediction performance could be attained by increasing the number of variables in the prediction problem and using 90%-10% data division. The results demonstrated the robustness of the DLNN model over the other AI models in handling the complex behaviour of concrete. Due to the high accuracy of the DLNN model, the developed method can be used as a practical approach for future use of CS prediction of EF concrete. (shrink)

Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three (...) AI models, including random forest, artificial neural network, and support vector machine for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process. (shrink)

Nowadays, higher education worldwide is affected by the COVID-19 pandemic. It has affected students’ attendance in the universities and causes universities to close down in more than 190 countries. On the other hand, novice engineers studied only a few lectures related to highway engineering. Their lectures have included very little knowledge about asphalt pavement construction as highway engineering consists of many areas that are not studied in detail during their studying years subject to their traditional education. Due to all mentioned, (...) a new drive to promote online learning paves the way to evaluate our future approach to curriculum development and delivery of educational materials for engineering courses. However, experts can offer solutions to these problems using their past experience. Hence, a system that allows experts to share their experience with other engineers after completing a project is needed. Nevertheless, the web-based expert system for maintaining flexible pavement problems in tropical regions designed in this study is a novel concept. Prior to developing this system, the need for such a system was determined through literature review and validated through a questionnaire survey. Experts were interviewed, and a questionnaire survey was conducted to construct the knowledge base of the system. Knowledge was presented as rules and coded in software through PHP programming. Web pages that support the user interface were designed using a framework that consists of CSS, HTML, and J-Query. Furthermore, the system was tested by an array of users engaged in highway engineering, namely, experts, teaching experts, novice engineers, and students. The mean values of the overall system evaluation performed by 20 users using a five-point Likert scale were 4, 4.5, 3.75, 4.25, 5, 4, and 3.5. Expert and user satisfaction prove the effectiveness of the proposed system. (shrink)