Chien-Chih Wang

Adding domestic waste into cement mortar or replacing fine aggregate can effectively reduce the use of natural sand and gravel and reduce carbon emissions, thereby preventing waste from polluting the Earth’s environment. This study explored a sustainable method for recycling television plastic shell waste (TPSW) by using it as a partial replacement for sand in cement mortar production. By evaluating water–cement ratios (0.4, 0.5, 0.6), ages (3, 7, 28, 56, 91 days), and TPSW levels (0%, 5%, 10%, 15%), this research assessed key properties, such as the slump, compressive strength, and durability. The results show that the TPSW absorbed less water than natural sand, increased the number of pores and slightly reduced the strength. However, a 5% substitution led to a minimal performance loss after 91 days, while it improved the sulfate resistance and resistivity. Overall, incorporating 5% TPSW reduces the environmental impact and carbon emissions, offering a sustainable solution for cement production.

This study explores a sustainable method for recycling television plastic shell waste (TPSW) by using it as a partial replacement for sand in cement mortar production. By evaluating water-cement ratios (0.4, 0.5, 0.6) and TPSW levels (0%, 5%, 10%, 15%), the research assesses key properties such as slump, compressive strength, and durability. Results show that TPSW absorbs less water than natural sand, increasing the number of pores and slightly reducing strength. However, a 5% substitution led to minimal performance loss after 91 days, while improving sulfate resistance and resistivity. Overall, incorporating 5% TPSW reduces environmental impact and carbon emissions, offering a sustainable solution for cement production.

The global industries are progressively transitioning towards low-carbon development; however, construction remains a significant contributor to energy consumption and carbon emissions. In promoting industrialized construction, the use of prefabricated buildings emerges as a crucial strategy for achieving environmental sustainability. This study initially examines the development and current status of prefabricated concrete component factories in Fujian Province, focusing on regional distribution and production conditions. It also gathers data on carbon emissions, time, and costs to formulate a multi-objective optimization model. Utilizing ant colony algorithms, the model aims to minimize costs while adhering to low-carbon principles and fostering the sustainable development of prefabricated buildings. The optimization results for slabs indicate a minimum production cost of RMB 5.7023 million, with associated carbon emissions of 1154.85 tons. Notably, a cost variation of RMB 10,000 can lead to a maximum difference of 50 tons in carbon emissions, emphasizing the importance of cost minimization as a primary objective. In comparison to conventional production, the optimization of collaborative production demonstrates reductions in both costs and carbon emissions. Furthermore, when focusing on normal and rush modes, costs can be reduced by over 20%, resulting in a potential decrease of up to 50% in carbon emissions. Consequently, effectively mitigating carbon emissions in component production is essential for enhancing the sustainability of the construction industry.

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.

In this study, the desulfurization slag used the volume method to replace fine natural aggregates in controllable low-strength materials (CLSM); the desulfurization slag content (DS) and curing time (t) were used as variables to test the compressive strength and surface resistivity of CLSM and simulated a prediction model on the results. The test results showed during that the compressive strength on the 28th day, the average desulfurization slag replacement amount increased by 10%, and the compressive strength decreased by 0.9 MPa. The surface resistivity increases with age, and each ratio increases from seven days to 28 days, and the surface resistivity value increases from 9.3% to 20.6%. After that, a hyperbolic function and exponential function with multiple variables were used to establish a simulation model of the effects of the DS content and curing time on the compressive strength and surface resistivity of CLSM. Compared with the test results, the statistical analysis shows that the average absolute percentage error (MAPE) of the compressive strength is 9.17%, and the surface resistivity is 10.67%. From the results, the predictive analysis model developed in this paper provides good predictive results in terms of compressive strength and surface resistivity.

A neural-based predictive model of the compressive strength of waste LCD glass concrete waste LCD concrete;compressive strength;back propagation neural networks;multivariable regression analysis;input variable strategy; The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.