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The objective of this presentation is to correlate the flow properties of a semi and concentrated solution of resin to its critical concentration. Viscosity of resin solution in various solvents was determined by using Haake rotovisco Searl type rotational viscometer. Values of zero shear viscosity, n0 and infinate shear viscosity, na were calculated by computing the viscosity data in Cross equation. Critical concentration, Ccrit of alkyd resin in individual solvents was derived from the plots of these parameters as function of concentration or weight fraction. The Ccrit values of the resin are found to be of higher order in good solvents and vice versa. There is no effect of shear thinning on critical concentration of the resin in a solvent. the dependence of these parameters on solvency power of solvents for the resin has been used for estimating solubility parameter of alkyd.

Organic Coatings are used for protection of metallic structures from corrosion. However they fail to isolate the substrate from corrosive materials present in the surroundings because the amount of water absorbed in coatings facilitates the movement of corrosive ions and gases through them, which in turn corrode the metal. The present studies illustrate the relative degree of permeation of chloride ions and water vapour through a variety of alkyd coating formulations.

Organic Coatings are used for protection of metallic structures from corrosion. However they fail to isolate the substrate from corrosive materials present in the surroundings because the amount of water absorbed in coatings facilitates the movement of corrosive ions and gases through them, which in turn corrode the metal. The present studies illustrate the relative degree of permeation of chloride ions and water vapour through a variety of alkyd coating formulations.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

The purpose of this work is to investigate the influence of curcumin dye natural colorant on adhesion, mechanical, thermal and electrochemical properties of blend poly (methyl methacrylate) (PMMA) – acrylic polyol.

Extracted curcumin yellow dye colorant from Curcuma Demostica was mixed with PMMA‐acrylic polyol blended polymer in the volume ratios of 9:1, 8:2 and 7:3. The mixtures were applied on pre‐treated cold‐roll mild steel panels. All of the paint coating samples were subjected to potential time measurement (PTM), rapid impact deformation, differential scanning calorimetry (DSC), cross hatch and Fourier transform infrared spectroscopy (FTIR) tests.

The addition of curcumin dye colorant was able to improve the adhesion, flexibilities and resistance against electrolytes penetration of the blended poly (methyl methacrylate) (PMMA) – acrylic polyol polymer paint system. Cross hatch test studies showed that high amount of curcumin dye colorant (AP30 paint system) had the lowest peel‐off coating area from the substrate. The FTIR test had confirmed the high concentration of hydroxyl group in the AP30 sample. The hydroxyl group was able to promote hydrogen bonding between coating substrate interface. The AP30 sample had the highest coating flexibilities when tested with rapid impact test. This was due to the lowest glass transition value T_g which indicated lowest cross linking density in the coating molecules structure. In the PTM test, AP30 paint system had shown the highest rate electrolytes penetration within the AP sample.

The composition of curcumin dye colorant in the polymer blend is limited from 10 percent to 30 percent pigment volume concentration. Increasing the amount of lawsone pigment will result inhomogeneous mixtures.

The AP paint system is suitable for interior applications. This paint system has to be mixed with suitable additive materials to improve its performance for exterior purpose.

This paper aims to report on the synthesis and characterisation of new flame retardants and anticorrosive additives based on Schiff’s base compounds, which were added physically to organic coating.

Flame retardants are incorporated into polymeric materials either as additives or as reactive materials. Additive-type flame retardants are widely used by incorporating into polymeric materials by physical means. In this research, Schiff’s base (azomethine) compounds are added physically to alkyd paint as flame-retardant additives. Elemental analysis, infrared spectroscopy and proton nuclear magnetic resonance spectroscopy were used to characterise the structure of the prepared Schiff’s base compounds. Thermal gravimetric analysis was used to evaluate their thermal stability. Experimental coatings were manufactured on a laboratory scale, and then applied by brush on wood and steel panels.

Results of an oxygen index value indicated that alkyd paints containing Schiff’s base compounds as additives exhibit very good flame-retardant effects. Also the physical, mechanical and corrosion resistance properties were studied to evaluate the drawbacks of the additives. The additives did not affect the flexibility of the paint formula. The gloss and the impact strength were decreased by the additives, but the hardness, adhesion and corrosion resistance were significantly improved by these additives.

Alkyd resins are the most extensively used synthetic polymers in the coating industry. Nitrogen compounds are a small but rapidly growing group of flame retardants which are in the focus of public interest concerning environment-friendly flame retardants. So, the focus of this study is on Schiff’s base compounds as flame retardants and anticorrosive additives for alkyd resins to assess their applicability.

Schiff’s base compounds can be used as new additives in paint formulations to improve the flame-retardant and corrosion properties.

In recent years, there has been considerable interest in the nitrogen-based family of materials because they not only have a wide range of thermal and chemical stabilities, but can also provide improved thermal and flame-retardant properties to polymers. The present paper reports on the synthesis and characterisation of Schiff’s base compounds and their performance in alkyd resin coatings.

The rheological properties of a coating are important determinants of storage, application and flow characteristics and, as such, the need for its accurate measurement is an important requirement in formulation development. Measurement of rheology is also an important phase of quality control testing, though frequently less sophisticated, more rapid, characterisation techniques may sometimes be used here. This article will consider some of the literature concerned with techniques of measuring coating rheology.

The eco-innovation is considered one of the possible ways to tackle climate change. However, the conflicting empirical evidence related to the role of eco-innovation on environmental quality becomes a motivation to explore the effect of eco-innovation on environmental degradation proxied by ecological footprint. Besides, it controls economic growth, remittance inflows, trade openness and total energy consumption in the environmental degradation function.

Uses the Augmented Auto Regressive Distributed Lag (AARDL) approach to examine the cointegration relation among the series during the period ranging from 1975 to 2017 for India within the environmental Kuznets curve (EKC) framework.

The result suggests that eco-innovation can mitigate climate change by reducing the ecological footprint. Similarly, economic growth reduces the ecological footprint in the short- and long-run. However, the square of economic growth is positive and significant. Thus, it shows evidence against the conventional EKC hypothesis. The results also reveal that remittance inflows have an insignificant negative role on the ecological footprint, while total energy consumption and trade openness harm the environment by enhancing the ecological footprint.

This study provides important implications for climate change mitigation. Thus, the government should promote eco-innovation to mitigate climate change by offering a favorable legal environment to the firms to adopt the same in their production and consumption activities. It also suggests that initiatives like green strategies should give serious attention while incurring research expenditure.

No prior studies assess the impact of eco-innovation on the ecological footprint for the period of 1975–2017 in India.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.