Over time, the change in the inflation rate causes cost overruns by deviating the prices of goods and services in construction projects that require practitioners to make budgeting revisions. Hence, this study aims to develop a construction rates forecasting model that can incorporate the changing impact of the inflation rate on construction rates and predict the prices in a particular year, which can be adjusted when developing the Bill of Quantities. Following the time series analysis standards, a mathematical model was developed using MATLAB for forecasting. Construction rates, building prices, labour wages and machinery rates were forecasted from 2020 to 2025 based on the data collected from 2013 to 2019. Akaike information criterion was used to validate the self-developed construction rate forecasting model. It was revealed that the model yielded better results when the construction rates were compared with the autoregressive integrated moving average time series model results. The rates forecasting model may be used for any construction project where rates are affected by the inflation effect.
The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (fc'), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of fc', fSTS and fFS of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study's findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.
The purpose of this research is to study the effects of quarry rock dust (QRD) and steel fibers (SF) inclusion on the fresh, mechanical, and microstructural properties of fly ash (FA) and ground granulated blast furnace slag (SG)-based geopolymer concrete (GPC) exposed to elevated temperatures. Such types of ternary mixes were prepared by blending waste materials from different industries, including QRD, SG, and FA, with alkaline activator solutions. The multiphysical models show that the inclusion of steel fibers and binders can enhance the mechanical properties of GPC. In this study, a total of 18 different mix proportions were designed with different proportions of QRD (0%, 5%, 10%, 15%, and 20%) and steel fibers (0.75% and 1.5%). The slag was replaced by different proportions of QRD in fly ash, and SG-based GPC mixes to study the effect of QRD incorporation. The mechanical properties of specimens, i.e., compressive strength, splitting tensile strength, and flexural strength, were determined by testing cubes, cylinders, and prisms, respectively, at different ages (7, 28, and 56 days). The specimens were also heated up to 800 °C to evaluate the resistance of specimens to elevated temperature in terms of residual compressive strength and weight loss. The test results showed that the mechanical strength of GPC mixes (without steel fibers) increased by 6-11%, with an increase in QRD content up to 15% at the age of 28 days. In contrast, more than 15% of QRD contents resulted in decreasing the mechanical strength properties. Incorporating steel fibers in a fraction of 0.75% by volume increased the compressive, tensile, and flexural strength of GPC mixes by 15%, 23%, and 34%, respectively. However, further addition of steel fibers at 1.5% by volume lowered the mechanical strength properties. The optimal mixture of QRD incorporated FA-SG-based GPC (QFS-GPC) was observed with 15% QRD and 0.75% steel fibers contents considering the performance in workability and mechanical properties. The results also showed that under elevated temperatures up to 800 °C, the weight loss of QFS-GPC specimens persistently increased with a consistent decrease in the residual compressive strength for increasing QRD content and temperature. Furthermore, the microstructure characterization of QRD blended GPC mixes were also carried out by performing scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS).
Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.
The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.
The polymer solar cells also known as organic solar cells (OSCs) have drawn attention due to their cynosure in industrial manufacturing because of their promising properties such as low weight, highly flexible, and low-cost production. However, low η restricts the utilization of OSCs for potential applications such as low-cost energy harvesting devices. In this paper, OSCs structure based on a triple-junction tandem scheme is reported with three different absorber materials to enhance the absorption of photons which in turn improves the η, as well as its correlating performance parameters. The investigated structure gives the higher value of η = 14.33% with Jsc = 16.87 (mA/m2), Voc = 1.0 (V), and FF = 84.97% by utilizing a stack of three different absorber layers with different band energies. The proposed structure was tested under 1.5 (AM) with 1 sun (W/m2). The impact of the top, middle, and bottom subcells' thickness on η was analyzed with a terse to find the optimum thickness for three subcells to extract high η. The optimized structure was then tested with different electrode combinations, and the highest η was recorded with FTO/Ag. Moreover, the effect of upsurge temperature was also demonstrated on the investigated schematic, and it was observed that the upsurge temperature affects the photovoltaic (PV) parameters of the optimized cell and η decreases from 14.33% to 11.40% when the temperature of the device rises from 300 to 400 K.
The majority of experimental and analytical studies on fiber-reinforced polymer (FRP) confined concrete has largely concentrated on plain (unreinforced) small-scale concrete columns, on which the efficiency of strengthening is much higher compared with large-scale columns. Although reinforced concrete (RC) columns subjected to combined axial compression and flexural loads (i.e., eccentric compression) are the most common structural elements used in practice, research on eccentrically-loaded FRP-confined rectangular RC columns has been much more limited. More specifically, the limited research has generally been concerned with small-scale RC columns, and hence, the proposed eccentric-loading stress-strain models were mainly based on the existing concentric-loading models of FRP-confined concrete columns of small scale. In the light of such demand to date, this paper is aimed at developing a mathematical model to better predict the strength of FRP-confined rectangular RC columns. The strain distribution of FRP around the circumference of the rectangular sections was investigated to propose equations for the actual rupture strain of FRP wrapped in the horizontal and vertical directions. The model was accomplished using 230 results of 155 tested specimens compiled from 19 studies available in the technical literature. The test database covers an unconfined concrete strength ranging between 9.9 and 73.1 MPa, and section's dimension ranging from 100-300 mm and 125-435 mm for the short and long sides, respectively. Other test parameters, such as aspect ratio, corner radius, internal hoop steel reinforcement, FRP wrapping layout, and number of FRP wraps were all considered in the model. The performance of the model shows a very good correlation with the test results.