A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

The utilization of recycled aggregates(RA)for concrete production has the potential to offer substantial environmental and economic advantages.However,RA concrete is plagued with considerable durability concerns,particularly carbonation.To advance the application of RA concrete,the establishment of a reliable model for predicting the carbonation is needed.On the one hand,concrete carbonation is a long and slow process and thus consumes a lot of time and energy to monitor.On the other hand,carbonation is influenced by many factors and is hard to predict.Regarding this,this paper proposes the use of machine learning techniques to establish accurate prediction models for the carbonation depth(CD)of RA concrete.Three types of regression techniques and meta-heuristic algorithms were employed to provide more alternative predictive tools.It was found that the best prediction performance was obtained from extreme gradient boosting-multi-universe optimizer(XGB-MVO)with R^(2) value of 0.9949 and 0.9398 for training and testing sets,respectively.XGB-MVO was used for evaluating physical laws of carbonation and it was found that the developed XGB-MVO model could provide reasonable predictions when new data were investigated.It also showed better generalization capabilities when compared with different models in the literature.Overall,this paper emphasizes the need for sustainable solutions in the construction industry to reduce its environmental impact and contribute to sustainable and low-carbon economies.

Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

Concrete is the most commonly used construction material.However,its production leads to high carbon dioxide(CO_(2))emissions and energy consumption.Therefore,developing waste-substitutable concrete components is necessary.Improving the sustainability and greenness of concrete is the focus of this research.In this regard,899 data points were collected from existing studies where cement,slag,fly ash,superplasticizer,coarse aggregate,and fine aggregate were considered potential influential factors.The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult.Instead of the traditional compressive strength test,this study combines five novel metaheuristic algorithms with extreme gradient boosting(XGB)to predict the compressive strength of green concrete based on fly ash and blast furnace slag.The intelligent prediction models were assessed using the root mean square error(RMSE),coefficient of determination(R^(2)),mean absolute error(MAE),and variance accounted for(VAF).The results indicated that the squirrel search algorithm-extreme gradient boosting(SSA-XGB)yielded the best overall prediction performance with R^(2) values of 0.9930 and 0.9576,VAF values of 99.30 and 95.79,MAE values of 0.52 and 2.50,RMSE of 1.34 and 3.31 for the training and testing sets,respectively.The remaining five prediction methods yield promising results.Therefore,the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete.Finally,the developed SSA-XGB considered the effects of all the input factors on the compressive strength.The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.

The Promotion of Building Culture in Rural Germany

Over the past two decades,building culture has increasingly caught the attention of the architectural and planning professions.The building culture is to be understood in the broadest sense as the sum of all cultural,economic,technological,social and ecological factors influencing the quality and process of planning and construc­tion.While the revitalisation and promotion of building culture is central to discussions on urban areas,these are generally ignored in regard to rural areas.This article aims to provide an overview of how building culture has been promoted in Germany’s rural areas,thereby contributing to international research on this topic.The paper adopts a general descriptive approach in examining the promotion of building culture in rural Germany.It provides back­ground knowledge on institutional promotion and demonstrates the diverse approaches implemented in represent­ative villages as best practices examples.The German experience confirms that the promotion of building culture is a meaningful and effective measure to help revitalise rural areas.Moreover,the three selected rural municipalities show how stakeholders from civil society are increasingly involved in measures to implement and promote building culture in the local context.

全球清洁能源快速增长引发对土地资源占用的担忧

Most countries in the world have joined the race for carbon neutrality, to meet the goals of the 2015 Paris Agreement, leading to a boom in the clean-energy sector. However, the construction of clean-energy infrastructure can disproportionately occupy limited productive soil and natural land resources, compared to conventional energy plants (Fig. 1). Thus, comprehending the land footprint resulting from the proliferation of clean-energy systems is now imperative, to better anticipate and manage the energy industry, and specifically to quantify the land-use trade-offs of the energy transition in a global context.