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This investigation explores the efficiency of composite coatings, leveraging polyvinyl alcohol (PVA) matrices embedded with zinc sulfide (ZnS) and cadmium sulfide (CdS) nanoparticles, for their infrared (IR) radiation blocking potential. Such coatings are strategically synthesized via a sol-gel method, targeting applications that demand IR attenuation, including but not limited to, construction, architectural fenestrations, vehicular glazing, and thermal insulation domains. In these composites, meticulous integration of ZnS and CdS nanoparticles within the PVA framework was demonstrated to significantly bolster their IR reflective or absorptive properties, consequently curtailing heat transference. It has been observed that nanoparticle concentration and coating thickness serve as critical factors, directly correlating with the IR-blocking proficiency--enhanced concentrations and augmented thicknesses invariably yield superior performance metrics. The surface morphology, assessed through Atomic Force Microscopy (AFM), revealed a positive correlation between nanoparticle concentration and surface roughness, paralleling an increase in particle size. This observation is corroborated by scanning electron microscopy, attesting to the uniform nanoparticle distribution. Fourier-transform infrared spectroscopy (FTIR) analysis identified novel peaks at approximately 1280 and 1700 cm-1, indicative of a chemical interaction between ZnS nanoparticles and the PVA matrix, as evidenced by the presence of reactive functional groups on the ZnS nanoparticle surface. Thermogravimetric analysis (TGA) imparted insights into the thermal stability of the specimens, with CdS composites exhibiting a weight loss of 98.73%, in stark contrast to the 91.04% manifested by the ZnS counterparts. The disparity is attributed to the higher boiling point of CdS (1750?) vis-à-vis ZnS (1700?), underscoring the material's intrinsic thermal resilience. The findings from this research underscore the potential of PVA-ZnS and PVA-CdS coatings as viable candidates for IR-blocking applications, positing an innovative solution to thermal management challenges in various sectors. [ABSTRACT FROM AUTHOR]

Accurately predicting and identifying appropriate parameters are necessary for producing a safe and reliable strength model of concrete elements confined with fiber-reinforced polymers (FRP). In this study, an extreme gradient boosting (XGBoost) algorithm was developed for the feature selection and prediction of the ultimate compressive strength of FRP-confined concrete. The modeling process was established using a dataset from open-source literature consisting of 490 circular columns. Three well-known artificial intelligence (AI) models, the multivariate adaptive regression spline (MARS), extreme learning machine (ELM), and RANdom Forest GEnRator (Ranger), were used to validate the proposed model. The results demonstrated the effectiveness of the XGBoost algorithm in the modeling process, selection of suitable parameters, and enhancement of the prediction accuracy. The algorithm achieved excellent prediction results for all input combinations with a coefficient of determination (R2) greater than 0.9, and the best performance is gained by using five input parameters with (R2 = 0.955), mean absolute percentage error (MAPE = 0.130), and root mean square error (RMSE = 0.572). The study revealed the flexibility and efficiency of capturing the nonlinear behavior of complex FRP-confined concrete using the proposed model. • Compressive strength of fiber-reinforced polymer-confined concrete was predicted. • Hybrid machine learning (ML) models were developed for the learning process. • ML models developed based on a dataset consisting of 490 circular columns. • Extreme gradient boosting (XGBoost) algorithm was used as robust feature selection. • The proposed hybrid ML model was provided robust prediction model. [ABSTRACT FROM AUTHOR]

Cost and time performance are considered to be the most important aspects in the construction industry. The exceptional conditions that took place in Iraq since the beginning of the third millennia had a huge vicious impact on the cost and time performance of local construction projects. This may represent the principal motivation for the local authorities to enact some four successive legislations in order to control the performance of the construction industry. In this research, an evaluation is made to the cost and time performance of local construction projects and their variation due to the multiple changes in the internal factors that affect project performance, and changes in the surrounding events include legislative, economic, and security environment during the period that lasted from 2003 to 2014. Data is collected from 30 governmental projects to conduct the evaluation. A comprehensive questionnaire is performed to estimate a quantitative value for the impact of several factors that concern both the owner and the contractor, with special consideration to their variation through the successive legislation periods. These estimates are, in turn, utilized in a system dynamics model, in which the project development process is simulated. The final cost and duration changes in the project are accumulated in the form of stocks to give an indication of the cost and time performance of the project. The developed model returned a progressive reduction of 10.9% for the change in project cost and 135.37% for the change in project schedule throughout the eleven years period. [ABSTRACT FROM AUTHOR]