scholarly journals Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jiandong Huang ◽  
Tianhong Duan ◽  
Yi Zhang ◽  
Jiandong Liu ◽  
Jia Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Coarse Aggregate ◽  
Functional Performance ◽  
Search Algorithm ◽  
Pervious Concrete ◽  
Mix Design ◽  
Design Parameters ◽  
Significant Variable ◽  
Correlation Parameters ◽  
Fold Cross Validation

Pervious concrete is an environmentally friendly material that improves water permeability, skid resistance, and sound absorption characteristics. Permeability is the most important functional performance for the pervious concrete while limited studies have been conducted to predict permeability based on mix-design parameters. This study proposed a method to combine the beetle antennae search (BAS) and random forest (RF) algorithm to predict the permeability of pervious concrete. Based on the 36 samples designed in the laboratory and 4 key influencing variables, the permeability of pervious concrete can be obtained by varying mix-design parameters by RF. BAS algorithm was used to tune the hyperparameters of RF, which were then verified by the so-called 10-fold cross-validation. Furthermore, the model to combine the BAS and RF was validated by the correlation parameters. The results showed that the hyperparameters of RF can be tuned by the BAS efficiently; the BAS can combine the conventional RF algorithm to construct the evolved model to predict the permeability of pervious concrete; the cement/aggregate ratio was the most significant variable to determine the permeability, followed by the coarse aggregate proportions.

Development of Photocatalytic Pervious Concrete Pavement for Air and Storm Water Improvements

2012 ◽  
Vol 2290 (1) ◽  
pp. 161-167 ◽  
Author(s):  
Somayeh Asadi ◽  
Marwa M. Hassan ◽  
John T. Kevern ◽  
Tyson D. Rupnow
Keyword(s):  
Flow Rate ◽  
Mechanical Performance ◽  
Concrete Pavement ◽  
Pervious Concrete ◽  
Mix Design ◽  
Unit Weight ◽  
Experimental Program ◽  
Design Parameters ◽  
Operational Parameters ◽  
Cleaning Agents

Self-cleaning, air-purifying pervious concrete pavement is a promising technology that can be constructed with air-cleaning agents with superhydrophilic photocatalyst capabilities, such as titanium dioxide. Although this technology has the potential of supporting environment-friendly road infrastructure, its effectiveness depends on a number of design and operational parameters that need to be evaluated. The objective of this study was to evaluate the mechanical, environmental, and mix design parameters that influence the performance and effectiveness of photocatalytic pervious concrete pavement. To achieve this objective, an experimental program was conducted in which the effects of relative humidity level, pollutants' flow rate, and mix design parameters, including void ratio and depth of the photocatalytic layer, were investigated. Mechanical performance tests included porosity, unit weight, permeability, and compressive strength. The environmental efficiency of the samples to remove nitrogen oxides (NOx) from the atmosphere was measured in the laboratory. Results of the experimental program showed that increasing the depth of the photocatalytic layer increased NOx reduction efficiency. In addition, NOx removal efficiency decreased with the increase in the pollutant flow rate and increased with the increase in ultraviolet light intensity.

scholarly journals IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rong Zhu ◽  
Yong Wang ◽  
Jin-Xing Liu ◽  
Ling-Yun Dai
Keyword(s):  
Principal Component Analysis ◽  
Random Forest ◽  
Cross Validation ◽  
Search Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Data ◽  
Learning Technology ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

Method to Investigate Mix Design Parameters of Pervious Concrete Mixtures

2016 ◽  
Vol 2577 (1) ◽  
pp. 43-52
Author(s):  
Hussein A. Kassem ◽  
Dima Z. Al Hassanieh ◽  
Maha Mrad ◽  
Ghassan R. Chehab ◽  
Majdi Abou Najm
Keyword(s):  
Pervious Concrete ◽  
Mix Design ◽  
Design Parameters ◽  
Concrete Mixtures

scholarly journals Mix Design and Engineering Properties of Fiber-Reinforced Pervious Concrete Using Lightweight Aggregates

2022 ◽  
Vol 12 (1) ◽  
pp. 524
Author(s):  
Chao-Wei Tang ◽  
Chiu-Kuei Cheng ◽  
Lee-Woen Ean
Keyword(s):  
Mechanical Properties ◽  
Coarse Aggregate ◽  
Pervious Concrete ◽  
Mix Design ◽  
Engineering Properties ◽  
Fine Aggregate ◽  
Fiber Reinforced ◽  
Split Tensile Strength ◽  
Coarse Aggregates ◽  
The Matrix

The main purpose of this study was to investigate the mix design and performance of fiber-reinforced pervious concrete using lightweight coarse aggregates instead of ordinary coarse aggregates. There were two main stages in the relevant testing work. First, the properties of the matrix were tested with a rheological test and then different amounts of lightweight coarse aggregate and fine aggregate were added to the matrix to measure the properties of the obtained lightweight pervious concrete (LPC). In order to greatly reduce the experimental workload, the Taguchi experimental design method was adopted. An orthogonal array L9(34) was used, which was composed of four controllable three-level factors. There were four test parameters in this study, which were the lightweight coarse aggregate size, ordinary fine aggregate content, matrix type, and aggregate/binder ratio. The research results confirmed that the use of suitable materials and the optimal mix proportions were the key factors for improving the mechanical properties of the LPC. Due to the use of silica fume, ultrafine silica powder, and polypropylene fibers, the 28-day compressive strength, 28-day flexural strength, and 28-day split tensile strength of the LPC specimens prepared in this study were 4.80–7.78, 1.19–1.86, and 0.78–1.11 MPa, respectively. On the whole, the mechanical properties of the prepared LPC specimens were better than those of the LPC with general composition.

scholarly journals Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Yuantian Sun ◽  
Guichen Li ◽  
Junfei Zhang ◽  
Deyu Qian
Keyword(s):  
Random Forest ◽  
Coarse Aggregate ◽  
Construction Material ◽  
Random Forest Model ◽  
Significant Variable ◽  
Rubberized Concrete ◽  
Forest Model ◽  
Environmental Friendly ◽  
Key Variables ◽  
The Relationship

Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study, an evolutionary random forest model (BRF) combining random forest (RF) and beetle antennae search (BAS) algorithms was proposed, which can be used for establishing the relationship between UCS of RC and its key variables. A total number of 138 cases were collected from the literature to develop and validate the BRF model. The results showed that the BAS can tune the RF effectively, and therefore, the hyperparameters of RF were obtained. The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). Furthermore, the variable importance was determined, and the results showed that the age of RC is the most significant variable, followed by water-cement ratio, fine rubber aggregate, coarse rubber aggregate, and coarse aggregate. This study provides a new method to access the strength of RC and can efficiently guide the design of RC in practice.

scholarly journals Mortars with Recycled Aggregates from Building-Related Processes: A ‘Four-Step’ Methodological Proposal for a Review

2021 ◽  
Vol 13 (5) ◽  
pp. 2756
Author(s):  
Federica Vitale ◽  
Maurizio Nicolella
Keyword(s):  
Building Materials ◽  
Thermal Analyses ◽  
Construction Materials ◽  
Physical And Mechanical Properties ◽  
Research Trends ◽  
Mix Design ◽  
Recycled Aggregates ◽  
Design Parameters ◽  
Natural Aggregates ◽  
By Products

Because the production of aggregates for mortar and concrete is no longer sustainable, many attempts have been made to replace natural aggregates (NA) with recycled aggregates (RA) sourced from factories, recycling centers, and human activities such as construction and demolition works (C&D). This article reviews papers concerning mortars with fine RA from C&D debris, and from the by-products of the manufacturing and recycling processes of building materials. A four-step methodology based on searching, screening, clustering, and summarizing was proposed. The clustering variables were the type of aggregate, mix design parameters, tested properties, patents, and availability on the market. The number and the type of the clustering variables of each paper were analysed and compared. The results showed that the mortars were mainly characterized through their physical and mechanical properties, whereas few durability and thermal analyses were carried out. Moreover, few fine RA were sourced from the production waste of construction materials. Finally, there were no patents or products available on the market. The outcomes presented in this paper underlined the research trends that are useful to improve the knowledge on the suitability of fine RA from building-related processes in mortars.

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