Comparative analysis of gradient-boosting ensembles for estimation of compressive strength of quaternary blend concrete

Concrete compressive strength is usually determined 28 days after casting via crushing of samples. However, the design strength may not be achieved after this time-consuming and tedious process. While the use of machine learning (ML) and other computational intelligence methods have become increasingly common in recent years, findings from pertinent literatures show that the gradient-boosting ensemble models mostly outperform comparative methods while also allowing interpretable model. Contrary to comparison with other model types that has dominated existing studies, this study centres on a comprehensive comparative analysis of the performance of four widely used gradient-boosting ensemble implementations [namely, gradient-boosting regressor, light gradient-boosting model (LightGBM), extreme gradient boosting (XGBoost), and CatBoost] for estimation of the compressive strength of quaternary blend concrete. Given components of cement, Blast Furnace Slag (GGBS), Fly Ash, water, superplasticizer, coarse aggregate, and fine aggregate in addition to the age of each concrete mixture as input features, the performance of each model based on R2, RMSE, MAPE and MAE across varying training–test ratios generally show a decreasing trend in model performance as test partition increases. Overall, the test results showed that CatBoost outperformed the other models with R2, RMSE, MAE and MAPE values of 0.9838, 2.0709, 1.5966 and 0.0629, respectively, with further statistical analysis showing the significance of these results. Although the age of each concrete mixture was found to be the most important input feature for all four boosting models, sensitivity analysis of each model shows that the compressive strength of the mixtures does increase significantly after 100 days. Finally, a comparison of the performance with results from different ML-based methods in pertinent literature further shows the superiority of CatBoost over reported the methods

Experimental and modelling of alkali-activated mortar compressive strength using hybrid support vector regression and genetic algorithm

This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete

Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed

Compressive and Flexural Strengths of Bio-Recycled Concrete Incorporated with Kenaf Fibre

ABSTRACTKenaf fiber (KF) has been applied in concrete to compensate for the weak tensile strength. Similarly, recycled concrete aggregate (RCA) is applied to reduce problems associated with waste concrete materials. However, both KF and RCA reduce the compressive strength (fck) of concrete. This study involves addition of calcite-producing bacillus subtilis bacteria to recycled aggregate concrete (RAC) incorporated with KF. The bacteria is added to concrete with varying proportions of RCA and KF to produce bio-fibrous recycled concrete (BFRC). The proportions of RCA are 0%, 25% 50% and 75% replacement of coarse aggregate, while KF are 0.2%, 0.5% and 1% of concrete. W/C of 0.45, 0.5, and 0.6 are applied to evaluate the effects of different w/c ratios. The properties evaluated are the fck and the flexural strength (ft) of the concrete samples at 28 days. The results showed that 0.2% KF in bio-concrete (BC) increases the fck by 12%; however, increasing above 0.2% decreases fck. KF in BC increases ft by 60%. Furthermore, 0.5% KF content resulted in highest ft of BRC. Increasing RCA content in bio-recycled concrete and BFRC decreases fck by 30% and 33%, respectively, as well as ft by 13% and 18%, respectively

Modelling of strength characteristics of silica fume/glass ternary blended concrete using destructive and non-destructive testing methods

The need to promote solid waste in concrete production prompted the use of waste glass powder (WGP) and silica fume (SF) powder in partial replacement for ordinary Portland cement in ternary blended concrete. The concrete was prepared with a total binder of 350 kg (in 1 m3) with 90% of cement and 10% the combination of silica fume and waste glass powder. The water/binder ratio was 0.42, and the fine aggregate to the total aggregate was 0.4. Models of wet density and 90-day strength development were presented in terms of age, WGP, and SF. Similarly, the concrete compressive strength and elastic/shear moduli were modelled for rebound numbers and response frequencies. The impact of geometrical shape and aspect ratio was also studied using cylindrical (100 - and 75-mm diameters) and rectangular specimens with an aspect ratio of 4 and 5. Transverse response frequency decreased with an increase in sample diameter and aspect ratio. The 90-day strength of C90G2.5S7.5 had a maximum strength of 55.5 MPa. Furthermore, the ternary blended sample (C90GyS10-y) was better than glass blended concrete (C90G10S0) in terms of 28-day strength, elastic and shear moduli, while the presence of SF (C90G0S10) decreased the response frequencies of OPC concrete (C100G0S0)

Potable Water Treatment in a Batch Reactor Benefited by Combined Filtration and Catalytic Ozonation

Due to continuous contamination of groundwater by anthropogenic activities, potable water fetches numerous pollutants such as pathogens, pharmaceuticals, and heavy metals, with these being severe health hazards. The main aim of the current study was to develop a hybrid unit based on catalytic ozonation and the filtration process to effectively remove the contaminants in drinking water. To the best of our knowledge, in the current study, the Fe-Zeolite 4A (Fe-Z4A)/O3 process followed by filtration involving rice husk and activated carbons were studied for the first time in order to treat drinking water. In the current investigation, fecal coliforms, arsenic, pharmaceuticals, turbidity, and TDS removal were investigated in a novel hybrid reactor. The results showed 100%, 45%, 40%, 70%, and 95% fecal coliform, arsenic, TDS, paracetamol, and turbidity removal efficiency, respectively. The results further indicated that all the studied drinking water samples followed WHO guidelines and NEQS for drinking water quality after the proposed treatment. Therefore, it is concluded that the proposed hybrid process implies a single unit is highly efficient for drinking water treatment. The designed novel hybrid reactor treatment can be scaled up in the future for household or commercial use

Synergistically Improved Catalytic Ozonation Process Using Iron-Loaded Activated Carbons for the Removal of Arsenic in Drinking Water

This research attempts to find a new approach for the removal of arsenic (As) from drinking water by developing a novel solution. To the author’s knowledge, iron-loaded activated carbons (Fe-AC) have not been previously applied for the removal of As in a synergistic process using ozonation and catalytic ozonation processes. The As was investigated using drinking water samples in different areas of Lahore, Pakistan, and the As removal was compared with and without using catalysts. The results also suggested that the catalytic ozonation process significantly removes As as compared with single ozonation and adsorption processes. Moreover, a feed ozone of 1.0 mg/min and catalyst dose of 10 g was found to maintain a maximum removal efficiency of 98.6% within 30 min. The results of the catalyst dose–effect suggested that the removal of As tends to increase with the increase in catalysts amount. Hence, it is concluded that the Fe-AC/O3 process efficiently removes As in water. Moreover, it was established that the Fe-AC/O3 process might be regarded as an effective method for removing As from drinking water compared to the single ozonation and adsorption processes

Performances of the Synergy of Silica Fume and Waste Glass Powder in Ternary Blended Concrete

The quest to enhance public health and the need for a reduction in the environmental solid wastes have prompted this study. Despite abundant studies on silica fume (SF or S) and waste glass powder (WGP or G), there is a need to understand the interaction of WGP with SF in the production of ordinary Portland cement (OPC or C)-based concrete using the water/binder ratio of 0.42. The investigated concrete comprised 90 wt.% of OPC and 10 wt.% of WGP+SF. The samples were denoted as C90GxS10−x such that x varied from 0–10 wt.% at the interval of 2.5. The findings revealed that an increase in the WGP/SF ratio enhanced the absorption of silica/glass blended concrete due to size incompatibility and proliferations of interfacial transition zones between the glass particle, silica fume and cement matrix. The density of fresh OPC concrete was higher than that of glass/silica blended concrete due to the difference in their relative densities. Incorporating WGP and SF in synergy enhanced silicate reorganization and led to a more amorphous binder and a reduction in hydroxyl-based compounds such as portlandite but caused microstructural heterogeneity in the morphology of the binder as obtained from XRD, FTIR and SEM/EDS results. The 28-day compressive strength of 46 MPa is achievable if the WGP and SF are kept within 2.5–5 wt.% and 5–7.5 wt.%, respectively. The study will foster the production of economic, environmental, and cost-efficient concrete

Synergistically Improved Catalytic Ozonation Process Using Iron-Loaded Activated Carbons for the Removal of Arsenic in Drinking Water

This research attempts to find a new approach for the removal of arsenic (As) from drinking water by developing a novel solution. To the author&rsquo;s knowledge, iron-loaded activated carbons (Fe-AC) have not been previously applied for the removal of As in a synergistic process using ozonation and catalytic ozonation processes. The As was investigated using drinking water samples in different areas of Lahore, Pakistan, and the As removal was compared with and without using catalysts. The results also suggested that the catalytic ozonation process significantly removes As as compared with single ozonation and adsorption processes. Moreover, a feed ozone of 1.0 mg/min and catalyst dose of 10 g was found to maintain a maximum removal efficiency of 98.6% within 30 min. The results of the catalyst dose&ndash;effect suggested that the removal of As tends to increase with the increase in catalysts amount. Hence, it is concluded that the Fe-AC/O3 process efficiently removes As in water. Moreover, it was established that the Fe-AC/O3 process might be regarded as an effective method for removing As from drinking water compared to the single ozonation and adsorption processes

Microstructural Characteristics, Modeling of Mechanical Strength and Thermal Performance of Industrial Waste Glass Blended Concrete

The need to get rid of solid waste in the environment necessitates the incorporation of waste glass powder (WGP) in mortar and concrete. The blending of WGP (G) with ordinary Portland cement (OPC) is a valorization technique that is not only cost efficient but also environmentally friendly. The replacement level is denoted as CxG10−x, where x is 0–20 wt.% at an interval of 5 wt.% in mortar (w/b = 0.4) and 0, 10, 20 and 30 in concrete (w/b = 0.42). The study investigates the effects of glass on the setting, workability, thermal resistance, microstructure, mineral phases and bond characteristics of silicon and hydroxyl-based compounds and C-O vibrations. It also provides the model equations for strength characteristics in terms of OPC, G and ages in mortar and concrete on one hand and investigates the residual strength and density of glass blended concrete at elevated temperature (550 °C) on the other. It is found that glass enhances the workability, reduces the setting time and density and enhances the residual strength and density of concrete. The presence of glass leads to the formation of coesite and microstructural distortion and decreases the Ca/Si ratio. Besides, the bond characteristics of the binder are significantly affected, while the thermal residual strength capacity in glass blended concrete (C80G20) is 40.4% and 75.14% lower than that in OPC concrete (C100G0) because of the low thermal conduction of glass particles. The optimum glass content in mortar and concrete to produce 33 MPa (28 days) and 47 MPa (90 days) is found to be 10 wt.% and 20 wt.%, respectively