Estimating the energy consumption for residential buildings in semiarid and arid desert climate using artificial intelligence.

This research aims to develop predictive models to estimate building energy accurately. Three commonly used artificial intelligence techniques were chosen to develop a new building energy estimation model. The chosen techniques are Genetic Programming (GP), Artificial Neural Network (ANN), and Evolutionary Polynomial Regression (EPR). Sixteen energy efficiency measures were collected and used in designing and evaluating the proposed models, which include building dimensions, orientation, envelope construction materials properties, window-to-wall ratio, heating and cooling set points, and glass properties. The performance of the developed models was evaluated in terms of the RMS, R2, and MAPE. The results showed that the EPR model is the most accurate and practical model with an error percent of 2%. Additionally, the energy consumption was found to be mainly governed by three factors which dominate 87% of the impact; which are building size, Solar Heating Glass Coefficient (SHGC), and the target inside temperature in summer.

Estimating the strength of bi-axially loaded track and channel cold formed composite column using different AI-based symbolic regression techniques.

Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt).

Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs.

The aim of this research is to present correction factors for the punching shear formulas of ACI-318 and EC2 design codes to adopt the punching capacity of post tensioned ultra-high-performance concrete (PT-UHPC) flat slabs. To achieve that goal, the results of previously tested PT-UHPC flat slabs were used to validate the developed finite element method (FEM) model in terms of punching shear capacity. Then, a parametric study was conducted using the validated FEM to generate two databases, each database included concrete compressive strength, strands layout, shear reinforcement capacity and the aspect ratio of the column besides the correction factor (the ratio between the FEM punching capacity and the design code punching capacity). The first considered design code in the first database was ACI-318 and in the second database was EC2. Finally, there different "Machine Learning" (ML) techniques manly "Genetic programming" (GP), "Artificial Neural Network" (ANN) and "Evolutionary Polynomial Regression" (EPR) were applied on the two generated databases to predict the correction factors as functions of the considered parameters. The results of the study indicated that all the developed (ML) models showed almost the same level of accuracy in terms of the punching ultimate load (about 96%) and the ACI-318 correction factor depends mainly on the concrete compressive strength and aspect ratio of the column, while the EC2 correction factor depends mainly on the concrete compressive strength and the shear reinforcement capacity.

Optimizing the superstructure configuration of highway bridges for cost-effective construction.

The structural progress of bridges in conjunction with efficiency has gained researchers' attention in the last few decades. Structures optimization applying mathematical analysis is utilized to achieve sustainability in the design and construction of bridges. Despite the extensive research in this area of knowledge, further structural optimization development needs to be developed. The main goal of this research is to develop a decision support system (DSS) that selects the optimum superstructure configuration for highway bridges, considering financial and technical parameters. The most common structural systems in the longitudinal and transverse directions of bridges are considered in this research. Simple and continuous spans are included in the longitudinal direction, while open and closed sections for the transverse direction. Different construction materials are considered as well, like reinforced concrete, pre-stressed concrete, steel sections, and composite sections, to achieve a wide diversity of alternatives. The developed DSS was illustrated graphically as a map for the optimum superstructure configuration for certain span and span to depth ratio combinations. These different configurations obtained from the DSS were mapped three times. The first was based on direct cost only, the second on construction time only, and the third on the total cost of each alternative. Eventually, the DSS was verified using collected case studies and proposed a convenient selection of bridge superstructure configurations within the considered range of span dimensions.

Modeling of the effect of gradation and compaction characteristics on the california bearing ratio of granular materials for subbase and landfill liner construction.

The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moisture content (OMC). For this reason, the particle packing and compactibility of the soil play a big role in the design and construction of subbases and landfills. In this research paper, experimental data entries have been collected reflecting the CBR behavior of granular soil used to construct landfill and subbase. The database was utilized in the ratio of 78-22% to predict the CBR behavior considering the artificial neural network (ANN), the evolutionary polynomial regression (EPR), the genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and the response surface methodology (RSM) intelligent learning and symbolic abilities. The relative importance values for each input parameter were carried out, which indicated that the (CBR) value depends mainly on the average particle size (D30, 50 & 60). They showed a combined influence index of 66% of the considered parameters in the model exercise. This further shows the importance and structural influence of the particles within the D50 and D60 range in a granular material consistency in the design and construction purposes. Performance indices were also used to study the ability of the models. The ANN model showed the best performance with accuracy of 88%, then GP, EPR and RF with almost the same accuracies of 85% and lastly the XGBoost with accuracy of 81%. Also, the RSM produced an R2 of 0.9464 with a p-value of less than 0.0001. These values show that the ANN produced the decisive model with the superior performance indices in the forecast of CBR of granular material used as subbase and waste compacted earth liner material. The results further show that optimal performance of the CBR depended on D50 and D60 for the design of subgrade, subbase, and liner purposes and also during the performance monitoring phase of the constructed flexible pavement foundations and compacted earth liners.

Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced machine learning approaches for subgrade and liner applications.

In the field of soil mechanics, especially in transportation and environmental geotechnics, the use of machine learning (ML) techniques has emerged as a powerful tool for predicting and understanding the compressive strength behavior of soils especially graded ones. This is to overcome the sophisticated equipment, laboratory space and cost needs utilized in multiple experiments on the treatment of soils for environmental geotechnics systems. This present study explores the application of machine learning (ML) techniques, namely Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and the Response Surface Methodology in predicting the unconfined compressive strength (UCS) of soil-lime mixtures. This was for purposes of subgrade and landfill liner design and construction. By utilizing input variables such as Gravel, Sand, Silt, Clay, and Lime contents (G, S, M, C, L), the models forecasted the strength values after 7 and 28 days of curing. The accuracy of the developed models was compared, revealing that both ANN and EPR achieved a similar level of accuracy for UCS after 7 days, while the GP model performed slightly lower. The complexity of the formula required for predicting UCS after 28 days resulted in decreased accuracy. The ANN and EPR models achieved accuracies of 85% and 82%, with R2 of 0.947 and 0.923, and average error of 0.15 and 0.18, respectively, while the GP model exhibited a lower accuracy of 66.0%. Conversely, the RSM produced models for the UCS with predicted R2 of more than 98% and 99%, for the 7- and 28- day curing regimes, respectively. The RSM also produced adequate precision in modelling UCS of more than 14% against the standard 7%. All input factors were found to have almost equal importance, except for the lime content (L), which had an average influence. This shows the importance of soil gradation in the design and construction of subgrade and landfill liners. This research further demonstrates the potential of ML techniques for predicting the strength of lime reconstituted G-S-M-C graded soils and provides valuable insights for engineering applications in exact and sustainable subgrade and liner designs, construction and performance monitoring and rehabilitation of the constructed civil engineering infrastructure.

Extensive overview of soil constitutive relations and applications for geotechnical engineering problems.

A state-of-the-art review has been conducted in this work on soil constitutive modeling, which has emphasized on: soil type, ground-water conditions, loading conditions, structural behavior, constitutive relation discipline, and dimensions. By extension also, the soil constitutive applications were reviewed on the bases of: single discipline dealing with soil mechanical properties constitutive modeling which included slope stability problems, bearing capacity, settlement of foundations, earth pressure problems, soil dynamics, soil structure interaction, thermal and hydrological conditions; bi-discipline (coupled problems) which solve problems related to thermomechanical (freeze/thaw conditions), smoothed particle hydrodynamics (SPH) and hydromechanical (consolidation, collapse and liquefaction) conditions in soils and rocks and multi-discipline constitutive models which solve complex problems related to thermo-hydromechanical (THM) conditions in soils and rocks. This work has shown that smoothed particle hydrodynamics (SPH) and hydromechanical (HM) models, which belong to bi-discipline or coupled conditions are better suited for geotechnical applications, generally, while thermo-hydromechanical (THM) models, which belong to multi-discipline are better suited to solving freeze/thaw and thermal piles problems and these are proven with high performance and flexibility.

Behavior of strip footing rested on undrained clay using consistency limits-based constitutive law.

The behavior of undrained clay was extensively studied by many earlier researchers. A lot of constitutive models were developed to describe the behavior of undrained clay based on its mechanical properties. The aim of this research is to present an innovative constitutive model for undrained clay based on its consistency limits and water content. The main concept of this model is to estimate the mechanical properties of clay using earlier correlations with consistency limits, then implement the estimated mechanical properties in a hyperbolic model and calibrate the hyperbolic parameters to match the failure criteria of the undrained clay. To verify the validity of the developed constitutive model, it was applied on a standard problem which is a strip footing rested on undrained clay layer, the results confirmed the ability of the model to simulate the nonlinear behavior of undrained clay up to ultimate condition. The main advantage of this constitutive model is the ability to capture the reduction of mechanical properties of clay with the increase in its water content, which makes it ideal to study the impact of seepage on shallow foundation.