Recycled aggregates from construction and demolition wastes as alternative filling materials for highway subgrades in China
TL;DR: Wang et al. as mentioned in this paper analyzed the physical and chemical properties of construction and demolition wastes (CDW) subgrade construction case in Beijing, and a series of tests (compaction degree test, settlement observation, Portable Falling Weight Deflectometer test) were carried out.
About: This article is published in Journal of Cleaner Production.The article was published on 2020-05-10. It has received 123 citations till now. The article focuses on the topics: Subgrade.
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TL;DR: In this paper, the authors present a comprehensive review on the workability and mechanical properties of fiber reinforced recycled aggregate concrete (FRAC) and highlight the most promising and feasible strength enhancement methods for the FRAC mainly using steel fiber, polypropylene fiber (PPF), basalt fiber (BF), and glass fiber (GF).
83 citations
TL;DR: In this article, a rubber-modified RAC (RRAC) notched beam specimens with three recycled aggregate substitutions (0, 50, and 100%), and four rubber contents (0., 2, 4, and 6) were tested using the three-point bending test.
67 citations
TL;DR: In this article, the fresh, mechanical, shrinkage, durability, and microstructural properties of cement-based materials when RBP partially replaces Portland cement are reviewed, based on the existing research, which shows that RBP is rich in SiO2, Al2O3, Fe 2O3 and has pozzolanic activity.
56 citations
TL;DR: In this paper, a review placed enormous emphasis on collating information from recent studies on biochars from agro-sources used as an admixture in cement-based applications.
Abstract: With increasing population and rising demands for improved built environment, there is an expected increase in greenhouse gas emission from the construction industry. Carbon dioxide emission levels are fast approaching a tipping point which could lead to irreversible climate change. The earth's capability to neutralise the CO2 emissions through the natural carbon cycle has been overstretched. Therefore it is imperative to adopt technologies that are able to capture and sequester CO2 in order to cancel out their release from industrial activities such as the construction and building industry. This is important so that cement-based material productions' carbon footprint can be reduced drastically for a positive change to take place in the climate. Biochar holds great promise as an effective CO2 sorptive material in cement-based applications relatively similar to its conventional use for soil amendment. Actually, fragmented researches on biochar as an admixture in cementitious materials have been conducted. Based on this logic, this review placed enormous emphasis on collating information from recent studies on biochars from agro-sources used as an admixture in cement-based applications. Similarly, the review gave up-to-date knowledge about the sources of the biomass and the production processes. Conclusively, the positive effects of biochar for carbon sequestration on some properties of the various cementitious applications were highlighted.
54 citations
TL;DR: In this paper, the variation law of the dynamic resilient modulus of C&D waste under repeated freeze-thaw cycles was revealed and a reasonable performance prediction model was established.
49 citations
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TL;DR: Wang et al. as mentioned in this paper conducted the CDW management analysis through 3R principle and investigated existing policies and management situations based on the reduction, reuse and recycle principles, which revealed that primary barriers of reducing CDW in China include lack of building design standard for reducing CDw, low cost for CDW disposal and inappropriate urban planning.
Abstract: Construction and demolition waste (CDW) accounts for 30% to 40% of the total amount of waste in China. CDW is usually randomly dumped or disposed in landfills and the average recycling rate of CDW in China is only about 5%. Considering there is big challenge in adoption of circular economy in CDW industry in China while related research is still limited, we conduct the CDW management analysis through 3R principle. Existing policies and management situations were investigated and analyzed based on the reduction, reuse and recycle principles. Results reveal that primary barriers of reducing CDW in China include lack of building design standard for reducing CDW, low cost for CDW disposal and inappropriate urban planning. Barriers to reuse CDW include lack of guidance for effective CDW collection and sorting, lack of knowledge and standard for reused CDW, and an under-developed market for reused CDW. As for recycling of CDW, key challenges are identified as ineffective management system, immature recycling technology, under-developed market for recycled CDW products and immature recycling market operation. Proposals to improve the current situation based on 3R principle are also proposed, including designing effective circular economy model, reinforcing the source control of CDW, adopting innovative technologies and market models, and implementing targeted economic incentives.
504 citations
TL;DR: In this article, a study was conducted at the Hong Kong Polytechnic University to investigate the possibility of using recycled concrete aggregates and crushed clay brick as aggregates in unbound subbase materials.
475 citations
TL;DR: This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS), as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationships between the various parameters.
Abstract: Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved. In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system's predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS), as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network (BPNN) and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses (MCS), Maximum tensile stresses (MTS), and Blow per foot (BPF). A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.
365 citations
TL;DR: A comprehensive laboratory evaluation of the geotechnical and geoenvironmental properties of five predominant types of construction and demolition (C&D) waste materials was undertaken in this article, and the results showed that these materials are suitable for reuse.
Abstract: A comprehensive laboratory evaluation of the geotechnical and geoenvironmental properties of five predominant types of construction and demolition (C&D) waste materials was undertaken in th...
336 citations
TL;DR: An overview of some soft computing techniques as well as their applications in underground excavations is presented and a case study is adopted to compare the predictive performances ofsoft computing techniques including eXtreme Gradient Boosting, Multivariate Adaptive Regression Splines, and Support Vector Machine in estimating the maximum lateral wall deflection induced by braced excavation.
Abstract: Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
287 citations