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Abstract The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA—a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.

Abstract Ground vibration due to blasting is identified as a challenging issue in mining and civil activities. Peak particle velocity (PPV) is one of the blasting undesirable consequences, which is resulted during emission of vibration in blasted bench. This study focuses on the PPV prediction in the surface mines. In this regard, two ensemble systems, i.e., the ensemble of artificial neural networks and the ensemble of extreme gradient boosting (EXGBoosts) were developed for PPV prediction in one of the largest lead–zinc open-pit mines in the Middle East. For ensemble modeling, several ANN and XGBoost base models were separately designed with different architectures. Then, the validation indices such as coefficient determination (R2), root mean square error (RMSE), mean absolute error (MAE), the variance accounted for (VAF), and Accuracy were used to evaluate the performance of the base models. The five top base models with high accuracy were selected to construct an ensemble model for each of the methods, i.e., ANNs and XGBoosts. To combine the outputs of the top base models and achieve a single result stacked generalization technique, was employed. Findings showed ensemble models increase the accuracy of PPV predicting in comparison with the best individual models. The EXGBoosts was superior method for predicting of the PPV, which obtained values of R2, RMSE, MAE, VAF, and Accuracy corresponding to the EXGBoosts were (0.990, 0.391, 0.257, 99.013(%), 98.216), and (0.968, 0.295, 0.427, 96.674(%), 96.059), for training and testing datasets, respectively. However, the sensitivity analysis indicated that the spacing (r = 0.917) and number of blast-holes (r = 0.839) had the highest and lowest impact on the PPV intensity, respectively.

Abstract To investigate the effect of water on the mechanical properties and acoustic emission (AE) characteristics of coal in the failure and deformation processes. Coal samples of different content were subjected to uniaxial compression tests and AE signals were monitored. The characteristics of the AE signals were further analyzed using fractal analysis. The results show that saturated coal samples have substantially reduced mechanical properties such as uniaxial compressive strength (UCS), dissipation energy, peak stress, and elastic modulus. Under loading, stress–strain curves are characterized by five distinct stages: (1) compaction; (2) linear elastic; (3) crack stable propagation; (4) crack accelerating propagation; and (5) post-peak and residual stages. Using phase-space theory, a novel Grassberger Procaccia (GP) algorithm was utilized to find the AE fractal characteristics of coal samples in different stages. It is significant to note that AE energy does not exhibit fractal characteristics in either the first or second stages. Contrary to the first two stages, the third stage showed obvious fractal characteristics. Fractal analysis of AE time sequences indicates that fractal dimension values change as stress increases, indicating the initiation of complex microcracks in coal. In the fourth stage, the fractal dimension rapidly declines as the strength reaches its limit, indicating the occurrence of macrocracks. However, fractal dimensions continued to decrease further or increased slightly in the fifth stage. Consequently, the coal begins to collapse, potentially resulting in a disaster and failure. It is, therefore, possible to accurately predict coal and rock dynamic failures and microcrack mechanisms by observing the subsequent sudden drop in the correlation dimension of the AE signals in response to different stages of loading.

Seismic liquefaction has been reported in sandy soils as well as gravelly soils. Despite sandy soils, a comprehensive case history record is still lacking for developing empirical, semi-empirical, and soft computing models to predict this phenomenon in gravelly soils. This work compiles documentation from 234 case histories of gravelly soil liquefaction from across the world to generate a database, which will then be used to develop seismic gravelly soil liquefaction potential models. The performance measures, namely, accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve, were used to evaluate the training and testing tree-based models’ performance and highlight the capability of the logistic model tree over reduced error pruning tree, random tree and random forest models. The findings of this research can provide theoretical support for researchers in selecting appropriate tree-based models and improving the predictive performance of seismic gravelly soil liquefaction potential.

Soil improvement is often done through mixing the granular soils with fine-grained materials to obtain densified soil matrix and eventually increase the bearing capacity of the soil. In this paper, sand was mixed with three different quantities of bentonite. Then, the hydromechanical behaviour of sand-bentonite mixtures was evaluated at different matric suctions. Suction-controlled triaxial test and the pressure plate extractor were used to obtain the experimental data needed to derive the stress-strain curves and the Soil Water Characteristics Curve (SWCC), respectively. The results showed that the apparent cohesion that developed due to the existence of matric suction was increasing with the bentonite content increase. Residual suction value was increasing as the bentonite content increasing. It was concluded that the increase of water retention capacity has made the sand-bentonite samples responds very significantly with changing of matric suction. Therefore, addition of fine materials to granular soils would make the soil prone to wetting collapse upon reaching the saturation condition during the rainy seasons.

The devastation caused by the illegal dumping and burning of tires has been staggering. In civil engineering, using tires engineering properties has become a major concern. For this investigation, the research used locally sourced tire chips and sand. Using tire chips sand as an alternative backfill material requires less pressure and has more improved properties than traditional backfill. Four specimens were utilized in this experiment: pure sand and sand mixtures containing 20%, 30%, and 40% tire chips, respectively. Both the Direct Shear and Triaxial Apparatus, two of the most important geotechnical tools, were used to compare and evaluate the shear properties of soil and sand tire chips. 50, 100, and 150 kPa Confining pressure and normal stress have been utilized to maintain a consistent stress level. Direct shear apparatus had a circular shape with an area of 16.62 cm2 and Triaxial shear apparatus had a height of 7.2 cm and a diameter of 3.2 cm. The stress-strain behavior of both apparatuses under ordinary loading and deviatoric stress was reported. The angles of internal friction (Φ′) and cohesion (c′) were measured for both equipment and specimens with and without tire chips, and the failure planes for direct shear and triaxial tests were reported. In both the direct and triaxial shear tests, 30% of the tire chips sand exhibit the best results, respectively. The addition of tire chips may significantly improve the toughness of the soil.

In the last decade, there has been an increase in research on ecologically benign, cost-effective, and socially useful cement alternative materials for concrete. Alternatives involve industrial and agriculture waste, the potential advantages of which may be recognized by recycling, repurposing, and recreating techniques. Important energy reserves and a decrease in Portland cement (PC) consumption may be attained by using these wastes as supplementary and substitute ingredients, contributing to a reduction in carbon dioxide (CO2) production. Consequently, the incorporation of marble dust powder (MDP) and calcined clay (CC) as supplementary cementitious material (SCM) in high strength concrete may lower the environmental effect and reduce the amount of PC in mixes. This study is conducted on concrete containing 0%, 5%, 10%, 15%, and 20% of MDP and CC as cementitious materials alone and in combination. The main objectives of this investigations are to examine the effect of MDP and CC as cementitious materials on the flowability and mechanical characteristics of high strength concrete. In order to examine the ecological effect of CC and MDP, the eco-strength efficiency and embodied carbon were considered. In this context, there are so many trial mixes were performed on cubical specimens for achieving targeted compressive strength about 60 MPa after 28 days. After getting it, a total of 273 concrete specimens (cubes, cylinders, and prisms) were used to test the compressive, splitting tensile, and flexural strength of high strength concrete correspondingly. Moreover, when the amount of MDP and CC as SCM in the mixture grows, the workability of green concrete decreases. In addition, the compressive strength, flexural strength, and splitting tensile strength are increased by 6.38 MPa, 67.66 MPa, and 4.88 MPa, respectively, at 10% SCM (5% MDP and 5% CC) over a period of 28 days. In addition, using ANOVA, response prediction models were generated and confirmed at a 95% level of significance. The R2 values of the models varied from 96 to 99%. Furthermore, increasing the amount of CC and MDP as SCM in concrete also reduces the amount of carbon embedded in the material. It is recommended that the utilization of 10% SCM (5% MDP and 5% CC) in high strength concrete is providing optimum outcomes for construction industry in the field of Civil Engineering.

The improvement of cement mortar’s thermal and mechanical properties has been greatly impacted by the addition of polymeric materials. However, polymers added to mortar shouldn’t impair either its mechanical or thermal conductivity properties. The main idea of this project is to insulate buildings by reinforcing their constituent mix with natural rubber latex (NRL) to reduce thermal conductance from excessive solar radiation which causes discomfort to building occupants. Consequently, this study presents experimental findings on the influence of natural rubber latex (NRL) on the properties of NRL-modified mortar. Five varying percentages of NRL (0.5%, 1.0%, 1.5%, 2.0% and 2.5%) were added into the mortar. Properties such as thermal conductivity, water absorption capacity, compressive and flexural strengths were evaluated. In addition, scanning electron microscopy was employed for the microstructural investigation. The experimental findings demonstrated that adding 2.5% NRL to mortar increased its thermal conductivity of mortar significantly thus enhancing its insulative properties. Even though adding NRL to mortar decreased the compressive and flexural strengths of some mixes, this wasn’t too substantial nor substandard. The tests that were executed demonstrate that the NRL has a huge potential to insulate cement mortar.

The use of foamed concrete (FC) in the construction sector has been rapidly growing over the past few years as a result of the several advantages it possesses in comparison to traditional high-strength concrete. FC, on the other hand, suffers from a number of deficiencies, such as brittleness, limited ductility, high porosity, excessive drying shrinkage, little resistance to cracking and deformation. To improve the tensile strength and fracture resistance of FC, engineers usually opt for steel fibre or polymer fibre as the reinforcement material of choice. Hence this research aims to investigate the potential utilization of synthetic twisted bundle macro-fibers (SF) in FC to enhance its durability, mechanical and thermal properties. The SF were included in the FC in varied amounts of weight fractions, including 0%, 1%, 2%, 3%, 4%, and 5% respectively. FC was produced at three low densities, precisely 1,000, 1,300, and 1,600 kg/m3, which were all prepared. Compression, flexural, splitting tensile, flow table, porosity, water absorption and thermal conductivity tests were conducted to establish the thermal, mechanical and durability properties of SF-reinforced FC. The findings imply that the integration of SF into FC results in a significant enhancement of the material’s strength and thermal conductivity properties while simultaneously lowering the material’s capacity for water absorption and porosity. For the purpose of improving the material’s mechanical, durability and thermal properties, the weight percentage of SF that was ideal ranged from 3% to 4%. The incorporation of SF into FC resulted in a rise in the material’s ductility, and the specimens maintained their integrity from the loading stage to failure. The SF is able to lessen the cracks that were already present in the FC and prevent the formation of additional cracks in the FC.

With an embodied carbon content of 0.93 kg CO2/kg, cement is an essential component of concrete. As the global demand for concrete construction is rising, research is being conducted to replace cement with supplementary cementitious materials (SCMs). A variety of SCMs are identified as having positive impacts on the concrete's compressive strength and embodied carbon. Coir Fibre Ash (CFA) is well known in SCM from existing research, but its use in concrete has not been investigated from an embodied carbon perspective before. The paper adopts an experimental methodology involving CFA as SCM at 3%, 6%, 9%, 12%, 15%, and 18% of 576 kg cement for each m3 of concrete. CFA was therefore added. The study aimed to find out the impact of the addition of CFA in concrete on compressive strength (CS), flexural strength (FS), splitting tensile strength (STS), and modulus of elasticity (MOE). The determination of embodied carbon was made, and RSM was used to develop the model with maximum accuracy. Samples were prepared for 7, 14, and 28 days. CFA was found to be positively affecting CS, FS, STS, and MOE till 9%, after which, because of the dominance of silica dioxide in CFA, it did not show any improvement. Embodied carbon was found to be decreasing with the increasing addition of CFA. RSM results followed by optimization provided highly validated equations for predicting the CS, FS, STS, and MOE of concrete by just using the value of CFA as SCM.

Ultra-high performance concrete (UHPC), an advanced class of fiber-reinforced cementitious material with extraordinary mechanical properties, low permeability, shrinkage and creep, and high energy absorption capacity, has seen steady increase in use, with applications covering construction of new members and retrofit of existing ones. Fibers are added in the UHPC mix to bridge cracks, carry tensile stresses, and contribute greatly to member ductility and load capacity. Hybrid fibers comprising micro and macro types are beneficial where the first type resists microcracking and the second targets macrocracking. This study investigates the effects of blending three fiber types, namely, hooked-end steel (referred to as type 1, representing macro fibers class), straight-end steel (type 2, intermediate size fibers), and carbon (type 2, micro size fiber), on the mechanical properties of UHPC. Experimental tests were performed to characterize the following mechanical properties: flowability, compressive strength, tensile strength, flexural strength, modulus of elasticity, and dry shrinkage. The primary variable in the tests was the blending of different fiber types, using either a unary form of type 1, a binary form of type 1 and 3 or type 2 and 3, and a ternary mix of all three types, at 1.56% dosage by volume. The mix with ternary fibers yielded a compressive strength, tensile strength, flexural strength, and modulus of elasticity that is 14%–17%, 14%–16.8%, 43.66%–22.16%, and 12%–16%, larger than the same respective properties of the mix with unary fibers. In addition, ternary fibers increased the cohesiveness of the mix by 17% and 26% compared to unary fibers.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

Certain disadvantages could have appeared while using hot mix asphalt (HMA), such as the release of unhealthy gases into the environment (environmental issues), difficulty in sustaining the temperature over long distances (logistical issues), and consuming a sufficient amount of energy while preparing and laying down (practical and economic issues). To overcome the aforementioned issues, this study aimed to develop rapid-curing emulsified asphalt cold mixes (EACM) comprising a cementitious filler made from industrial by-product materials. Paper sludge ash (PSA) is used as an active filler for application in the EACM rather than conventional mineral filler. Additionally, to maximize the effect of PSA’s hydraulic activity, incinerated sewage ash (ISA) is utilized as an activator at a concentration of 0%–4% by mass of the aggregates. The results demonstrate that the use of waste PSA significantly improves the indirect tensile stiffness modulus (ITSM) by around 10 times more after 2 days than the traditional emulsified asphalt cold mixes. In addition, the improvement in ITSM was around 30% and 65% for 6%PSA+1%ISA and 6%PSA+4%ISA mixes, respectively. Furthermore, the rutting for the 6%PSA+1%ISA and 6%PSA+4%ISA mixes decreased to around 19% and 11% in comparison to the traditional 131-pen HMA. The formation of hydration products and rapid demulsification of asphalt emulsion, which results in binding within the mixtures, are responsible for the increased ITSM and rutting resistance. As a result, environmental issues are minimized, and energy preservation may be maintained.

Textile reinforced mortar (TRM) is mainly used for strengthening of existing structural members whereas, on the other hand Textile reinforced concrete (TRC) is a technology implied in construction of new members for enhancing the structural behaviour. Application of TRM on the tension zone of the reinforced concrete (RC) slabs to improve the flexural capacity has been investigated by many researchers in the past. However, the effectiveness of textile fabrics, used as internal reinforcement in the RC slab (TRC technology) needs to be studied. The paper, therefore, presents the experimental research conducted on three one-way RC slabs specimens reinforced using textile grid. An innovative Polyacrylonitrile (PAN) based carbon textile grid was used as internal reinforcement in combination with the steel bars. Two textile-reinforced RC slabs having one and two layers of textile grid (SRC + 1T and SRC + 2T respectively) and one reference slab (SRC) was fabricated to investigate the flexural behaviour under a four-point loading system. The internal textile reinforcement layer(s) was confirmed to be effective, particularly in terms of improving the cracking load, ductility, deformability and toughness. The material ductility of SRC + 1T and SRC + 2T slabs were increased by 41% and 44% compared to SRC slab. Also, the deformability ratio was found to be greater than 4, indicating a ductile failure of textile-reinforced slabs. Further, based on the load-deflection relation, moment-curvature curves were derived. Moreover, these curves were also developed using Eurocode two prediction model. The experimental and the predicted moment-curvature curves showed good agreement.

In the present era of architecture, different cross-sectional shapes of structural concrete elements have been utilized. However, this change in shape has a significant effect on load-carrying capacity. To restore this, the use of column confinements with elliptical sections has gained attention. This paper aim to investigate the effect of elliptical shape sections of confined concrete reinforced with Carbon Fiber Reinforced Polymer (CFRP) and steel tube on axial load-carrying capacity. This study is achieved using following tools Finite Element (FE) in Abaqus and Artificial Neural Networks (ANN) modeling. The study involved a 500-mm-high column with three sets of aspect ratios: 1.0, 1.5, and 2.0. In each aspect ratio, three different layers of CFRP were used, i.e., .167, .334, and .501-mm. Analytical results showed that with the increase in aspect ratio from 1 to 2, there is a decrease in ultimate axial load of about 23.2% on average. In addition, the combined confining pressure of steel tube and CFRP increases with a decrease in dilation angle as the number of CFRP layers increases. The failure mode for the column with a large aspect ratio is local buckling at its mid-height along the minor axis. The result showed a good correlation between FE and experimental results of ultimate stress and strains, with a mean squared error of 2.27 and .001, respectively. Moreover, ANN and analytical models showed a delightful correlation of R2 of .97 for stress models and .88 for strain models, respectively. The elliptical concrete section of the column confined with steel tubes can be adopted for a new architectural type of construction; however, with more than three aspect ratios, the wrapping of the section with CFRP jackets is highly recommended.

The shear behavior of concrete beams is highly affected by the implementation of better performance concrete. Hybrid fibers addition to concrete mixture has proven to improve the performance compared to just using single type of fiber. Thus, in this current study, the shear behavior of hybrid-fiber-reinforced-high-strength-concrete beams was investigated experimentally. In addition, the effect of the span-to-depth ratio and the transverse reinforcement ratio were examined. Results showed that, when .45% of the cement weight is replaced with polypropylene fiber and 7% of the cement weight is replaced with steel fibers, the shear strength of the beam was enhanced by 18% in comparison to the control beam. The Formation and progression of cracks were also better controlled. The behavior of hybrid-polypropylene-steel-fibers-high-strength-concrete beams was observed to be comparable to that of conventional concrete ones as the shear strength increased with the decrease in span to depth ratio or the increase in transverse reinforcing ratio. A non-linear numerical model was developed and validated using the experimental results. The shear capacities of beams were calculated using ACI, which was compared to experimental and numerical results. The ACI’s calculations were conservative when compared with the experimental or numerical results. The coefficient of variance between the ACI and experimental shear capacity results was 4.8%, while it was 9.2% between the ACI and numerical shear capacity results.

This article examines the performance of Carbon Fibre Reinforced Polymer (CFRP) on Concrete Filled with Polymer Vinyl Chloride Tube (CFPT) columns under axial compression. Firstly, 44 CFPT specimens from the literature were analyzed using ABAQUS software to understand the compressive behavior of specimens under applied displacement. Secondly, 268 CFPT specimens are simulated to understand the influence of CFRP on these control specimens with a varying number of FRP layers and wrapping depth. Other variables such as the unconfined concrete strength, the thickness of the PVC tube, and the size and slenderness ratio of the columns were also studied. Studies are extended to confinement damage plasticity model analysis of CFRP-CFPT (CCFPT) columns. Relationships between the load-carrying capacity of CCFPT columns and the CFRP properties were developed. The effect of these parameters on the CFPT leads to the development of analytical models. It is an advantage to applying a such new type of composite columns in various applications.

The performance of waste marble powder as a partial replacement for cement is examined with the aim to achieve more sustainable concrete. Pursuant to this goal, a total of 15 specimens were manufactured and then tested to examine the bending behavior. The effects of longitudinal reinforcement ratio and waste marble powder ratio were selected as variables. The experimental results showed that different proportions of tension reinforcement and waste marble powder had different crack and bending impacts on reinforced concrete beams. As the waste marble powder amount in the concrete mixture is increased from 0% to 40%, it was detected that the crack type changes from a shear crack from to a flexural crack as the amount of waste marble powder increases in the mixing ratio. The experimental findings revealed that the waste marble powder can be successfully used as 10% of the partial replacement of cement. Increasing the waste marble powder ratio by more than 10% can significantly decrease the capacity of the beams, especially when longitudinal reinforcement ratio is high. The influence of waste marble as partial replacement on the capacity decreases as the longitudinal reinforcement ratio decreases. Therefore, 10%–20% marble waste can be utilized as a replacement for cement when the longitudinal reinforcement ratio is close to the balanced ratio and more than 20% waste marble ratio should be avoided for any cases.

In this study, it is aimed to reveal how anchored and unanchored non-structural elements (NEs) in reinforcement concrete (RC) library structures change the earthquake behaviors of these structures. A library structure is selected for three-dimensional (3D) analysis. First, seismic shake table tests are performed for selected NEs modeled with/without anchorage to the structure. Owing to these seismic tests, the largest seismic displacement values that occurred during 10 different earthquakes on each anchored and unanchored NE are acquired. Then, special seismic loads (e.g., high bookcase loads and heavy table loads) of anchored and unanchored NEs are calculated, taking into account the ASCE/SEI 7-16 seismic design code and the 2018 Turkish Building Earthquake Code (TBEC). Seismic spring values of NEs are calculated using the obtained seismic forces and maximum seismic displacements for the NEs. Then, these spring values are applied to the base of the NEs in the 3D model of the structure, and 10 different earthquake analyses are conducted for the library structures with anchored and unanchored NEs. According to the numerical results, the seismic effects of NEs on the earthquake behavior of RC library structures are evaluated for two different seismic design codes in detail, and it is concluded that NEs should not be neglected while modeling and analyzing RC library structures. It is seen from the seismic analyses that for the TBEC, 37, 56, and 126 mm maximum seismic displacements are observed on the library structure for structures without NEs, structures with unanchored NEs, and structures with anchored NEs, respectively. Moreover, for the ASCE code, 32, 45, and 119 mm maximum displacements are seen on the library structure for structures without NEs, structures with unanchored NEs, and structures with anchored NEs, respectively. It is clearly understood that anchored or unanchored NEs have different seismic effects on the 3D earthquake behavior of library structures.

Confined concrete in pipes provides a solution for the structures to resist lateral forces and avoids the problems associated with production of confined concrete and lateral reinforcement. To evaluate the influence of PVC pipe on the compressive behavior of concrete filled composite tubes, ABAQUS software was used to simulate 44 concrete filled PVC pipes (CFPT). The influence of internal steel reinforcement, unconfined concrete strength, slenderness ratio, specimen’s size and thickness of PVC tube on failure mode load carrying capacity, and strain of PVC confined reinforced concrete column was studied. The present discussion show that the existing analytical models failed to capture the effect of these parameters. On the other hand, the proposed finite element (FE) models achieve a very good agreement between the experimental and analytical values. The proposed FE model can provide an acceptable portrayal of the CFPT’s response. The response of confined concrete was estimated using trial and error approach and as a result a model for the strain of confined concrete was proposed. The effect of these parameters on the CFPT leads to development of analytical models.

Concrete structures frequently experience the phenomena of crack development. The researchers used certain healing agents to boost the frequently observed autogenous crack-healing capacity of concrete. Although various artificial intelligence (AI) techniques have been used to forecast a number of concrete properties, the application of AI to forecast self-healing capacity of engineering cementitious composites (ECC) is relatively rare. For this purpose, three gene expression programming (GEP) models were created to predict the potential of admixture-based concrete to self-heal. A total of 619 data points were extracted from the literature with four contributing factors i.e., amount of fly ash (FA), silica fume (SF), limestone powder (LP), and crack width before self-healing (CWB) in order to forecast the crack width after self-healing (CWA). The data points were divided into training (70%) and testing (30%) sets. Various performance indicators were employed to assess the efficacy of the derived GEP models, which includes coefficient-of-correlation (R), relative-root-mean-square-error (RRMSE), root-mean-squared-error (RMSE), root-mean-squared-logarithmic-error (RMSLE), Nash-Sutcliff efficiency (NSE), root-squared-error (RSE), mean-absolute-error (MAE), performance-index (PI), and objective-function (OBF). The best optimized GEP model (SHC-GEP 1), was found with R = 0.938, NSE = 0.944, RMSE = 2.799 µm, MAE = 3.72 µm, RMSLE = 0.006 µm, RSE = 0.124 µm, and RRMSE = 0.439 µm, in the testing phase. While the OBF was less than 0.2 (i.e., 0.119), indicating that the model is free from overfitting issue. In contrary, the conventional linear regression model fails to meet this criterion and gives many negative predicted values. Moreover, the sensitivity analysis results indicate that the CWB has the greatest impact on the CWA. Also, the parametric trends between input variables and CWB, are in-line with the previous literature, indicating the robustness of the established model. Additionally, the suggested GEP model may be used as a cutting-edge alternative for forecasting the final crack width after concrete self-heals, helping engineers to assess the crack-reduction capability. Furthermore, it is recommended to explore the crack healing capabilities of other supplementary cementitious material like bagasse ash, wheat straw ash, and soda glass powder, subjected to modeling their healing ability using AI techniques.

In this research, it is studied the crack and flexural behavior of reinforced concrete beams with various bottom ash ratios (BARs) considered as fine aggregate in an experimental and numerical investigation. For experimental purposes, different concrete series are considered varying aggregate sizes ranging from 0 to 25 mm. To supplement concrete, bottom ash is put to use in conjunction with material from 0–5 mm in size aggregate particles as replacement for fine aggregates with ratios of 25%, 50%, 75%, and 100%. Experiments were done to investigate the behavior of the beams and how flexural and fracture behaviors are represented. 75% BARs gave optimum results in terms of displacement capacity. Increasing BAR to 100% decrease deflection capacity of the beam. Also, ANSYS software is used to build 3D finite element models (FEMs) of beams to compare with experiment data. Experimental and 3D numerical tests show exceptionally tight flexural and fracture behaviors. Following this, a computer-generated structure is made by running SAP 2000, and the strength of the beams is then utilised in an RC structural model. Every stage of the building’s construction is thoroughly assessed utilizing multiple types of seismic testing, employing the SAP2000 program, with the resulting analysis providing significant findings on how the seismic force of 75% BAR affects horizontal displacement of each floor. The results showed that the weight of the structure dramatically decreases as the number of columns and RCBs are raised while also increasing the number of BARs. Moreover, the magnitude of earthquake and BAR have a significant effect on the horizontal displacement behavior of reinforced concrete structures. The strength of the concrete structure varies between close- and far-fault earthquakes, and for close-fault earthquakes, concrete strength is stronger than for far-fault earthquakes. This brings us to the second disadvantage of BAR which is the 75% strain produces a severe displacement of reinforced concrete structures. Besides, it was seen that the simulations and experiments yield tiny cracks with very identical configurations.

In this research work, different combinations of normal strength concrete (NSC), ultra-high-performance concrete (UHPC), and steel fiber-reinforced UHPC (SFR-UHPC) concrete with re-bars of conventional steel and of carbon fiber-reinforced polymer (C-FRP) are used in a two-way square slab of size 1000mm x 1000mm x 75mm subjected to 2500 mm free-fall impact loading. Experimental arrangement consisting of 105 kg dropping weight with the circular flat impacting face of 40 mm diameter used for carrying out impact test is modeled using a high-fidelity physics-based finite element computer code, ABAQUS/Explicit-v.6.15. After validating the experimental results of the NSC slab with steel bars, analyses are extended by replacing NSC and steel bars with UHPC/SFR-UHPC and C-FRP bars, respectively, under the same dropping weight. Only the remote face (tension face) of the slabs is provided with the re-bars. Widely employed and available with the ABAQUS, the Concrete Damage Plasticity model with strain-rate effects has been entrusted for simulating the concrete plastic response. Re-bars of steel are idealized with the Johnson-Cook plasticity damage model. C-FRP re-bars are defined with the classical plasticity model following the elastic-plastic constitutive laws. The impact responses of the slabs consisting of NSC/UHPC/SFR-UHPC concrete with re-bars of steel, and C-FRP combinations considered are discussed and compared. Slabs made of UHPC/SFR-UHPC concrete with the C-FRP re-bars are found to offer a promising combination of materials to withstand low-velocity impact load with little damage and extraordinary impact performance.

With the increasing number of vehicles in the world, the amount of waste tires is increasing day by day. In this case, the disposal of expired tires will cause serious environmental problems. In recent years, instead of disposing of tire wastes, most of them have been started to be recycled to produce fiber-reinforced concrete. Thus, steel fibers recovered from waste tires have been preferred as an alternative to industrial steel fibers due to their environmentally friendly and low-cost advantages. In this study, an experimental study was carried out to explore the effect of fiber content on the fresh and hardened state of the concrete. To achieve this goal, compression, splitting tensile, and flexure tests were carried out to observe the performance of the concrete with tire-recycled steel fibers with the ratios of 1%, 2% and 3%. There is an improvement in the mechanical properties of the concrete with the increase of the volume fraction of the steel fiber. However, a significant reduction in workability was observed after the addition of 2% steel fibers. Therefore, it is recommended to utilize 2% tire-recycled steel fibers in practical applications. Furthermore, experimental results of concrete with tire-recycled steel fibers were collected from the literature and empirical equations based on these results were developed in order to predict the compressive and splitting tensile strengths.

The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbor (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.

Exposure of building infrastructures to accidental or intentional blasts is an extreme load condition that may cause irreparable damage leading to the collapse of buildings. Columns being principal elements are the most important for the stability and safety of the buildings under accidental explosions and subversive blast events and therefore attract the attention of structural engineers and researchers. Some recent examples are the Beirut seaport explosion (August 2020), the explosion at an ammunition warehouse in Ryazan City of Russia (October 2020), the gas explosion in China’s Hubei Province (June 2021), a blast at a chemical factory on the outskirts of Bangkok (July 2021), and the explosion on a container ship docked at Dubai’s Jebel Ali Port (July 2021). In the crises like ongoing conflict between Russia and Ukraine, the enhanced response of the principal components of a structure may save the life of the building users by limiting severe damage to the structure. In this study, three experimentally tested 3000-mm-long normal strength concrete columns, 300mm x 300mm, provided with (i) conventional reinforcement, (ii) seismic reinforcements over top and bottom confining regions (600 mm), and (iii) seismic reinforcement over confining and mid-height regions, carrying an axial working load of 950 kN available in the literature, are modeled in the ABAQUS 2020 code and are subjected to 82 kg TNT close-range explosive load at a scaled distance 1.0 m/kg1/3 using the software’s explicit module. In addition to this, one column with seismic reinforcement over its entire length has been considered and modeled. The concrete damage plasticity model is explored for nonlinear elastic and inelastic behaviors, degradation of stiffness, and loading rate effect on concrete. Following the validation of the numerical models, the seismic reinforcements of the columns have been replaced by the cross-diagonal reinforcements between the conventional stirrups with the same axial load. Blast performance of the columns with the seismic reinforcements and with replaced diagonal reinforcements is critically examined and discussed. The results show that the application of cross-diagonal reinforcements as a replacement for the seismic reinforcements enhances the blast resistance of the reinforced concrete column significantly by reducing the damage and displacement.

Tall buildings with irregular shapes and considerable heights are gaining popularity in creating the vertical cities around the world. They also considered one of the major energy consumers with little regards to sustainability. Tall building is wind-sensitive structure and shape plays major role in determining wind loads, which usually govern the design of its lateral resisting system. Thus, evaluating wind loads properly and designing an optimal lateral system accordingly are the main challenges attributed to the design process of irregular tall building. This paper presents a computational procedure for the optimal design of wind-resistant irregular tall building to minimize the total weight of structure within design requirements in single digital environment. That is achieved firstly by creating a digital system of computational fluid dynamic (CFD) analysis that is coupled with pressure-load translation (PLT) algorithm to evaluate the wind motions on irregular tall buildings and generate the design wind loads accordingly. Genetic Algorithm (GA) with enhance design constrains function of lateral displacements, inter-story drifts and top acceleration is then developed to perform structural optimization. A numerical example using 70-story twisting reinforced concrete building is implemented to verify the feasibility of the developed computational procedures. Steady and incompressible flow applied at (0°) angle of attack was implemented in the CFD model to simulate the wind flow on the studied building. Genetic algorithm with improved design constraints of static and dynamic design requirements was developed to optimize the structure effectively and efficiently. The numerical example demonstrates its effectiveness by achieving 35.71% reduction of concrete volume from the original lateral structural system design. This is also translated into a sustainability value by lessening the embedded carbon dioxide by 4,400 tons.

The study aims to conduct a direct pull-out test on fifty-four cube specimens considering different variables, including the type of reinforcement (sand-coated glass fiber-reinforced polymer (GFRP) and ribbed steel bars); the type of concrete (normal weight concrete NWC and lightweight foamed concrete LWFC); the diameter of the reinforcing bars (10 mm; 12 mm; and 16 mm) and the bonded length (3∅, 4∅, and 5∅). The hybrid fiber hooked-end steel (0.4% by volume) and polypropylene (0.2% by volume), respectively were used to improve the properties of LWFC by converting the brittle failure to ductile. The results showed that in the case of strengthened foamed concrete (FC), the bond strength with steel bars was greater compared to that with the GFRP bars. The bond strength ratio between the GFRP and steel bars of the FC specimens was found to vary between 37.8–89.3%. Additionally, in all specimens of FC, pull-out failure was witnessed with narrower crack width compared to NWC. Furthermore, mathematical equations have been proposed for predicting the bond strength of FC with steel and GFRP bars and showed good correlation with the experimental results.

This study assesses hydration, rheology, and sorptivity of cementitious pastes using relatively low-cost Arduino-based platforms with integrated sensors. The prime objective is to develop a correlation between conventional apparatus-based and Arduino-integrated sensor-based assessment. A total of six samples of cementitious paste were prepared at a room temperature of 26°C, with a w/b ratio of 0.3, containing cement, fly ash, and silica fume. First, the conventional apparatus was employed to assess hydration, rheology, and sorptivity of cementitious mixes. Afterward, a platform arranged for data acquisition, comprising Arduino Mega 2560, a temperature sensor, a soil moisture sensor, and a voltage supply, was used to assess the same properties. The recorded temperature and moisture content data were transmitted using the Android application and the Wi-Fi modem router. A 5-minute moisture analysis test was conducted to monitor the rheological behavior of cementitious mixes. The heat of the hydration mechanism was evaluated for 32 hours using a temperature sensor, enabling continuous and real-time monitoring. Moreover, a sorptivity test on cube samples was performed using shielded self-based apparatus in an adiabatic condition, resulting in relatively long-term monitoring. The microscopic details are analyzed by using a scanning electron microscope (SEM) in conjunction with EDX analysis. Thus, the potential application of the inexpensive sensor-based method is verified.

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R2 = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R2 = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R2 = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R2 = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R2 = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.

Portland cement (PC) is considered the most energy-intensive building material and contributes to around 10% of global warming. It exacerbates global warming and climate change, which have a harmful environmental impact. Efforts are being made to produce sustainable and green concrete as an alternative to PC concrete. As a result, developing a more sustainable strategy and eco-friendly materials to replace ordinary concrete has become critical. Many studies on geopolymer concrete, which has equal or even superior durability and strength compared to traditional concrete, have been conducted for this purpose by many researchers. Geopolymer concrete (GPC) has been developed as a possible new construction material for replacing conventional concrete, offering a clean technological choice for long-term growth. Over the last few decades, geopolymer concrete has been investigated as a feasible green construction material that can reduce CO2 emissions because it uses industrial wastes as raw materials. GPC has proven effective for structural applications due to its workability and analogical strength compared to standard cement concrete. This review article discusses the engineering properties and microstructure of GPC and shows its merits in construction applications with some guidelines and suggestions recommended for both the academic community and the industrial sector. This literature review also demonstrates that the mechanical properties of GPC are comparable and even sometimes better than those of PC concrete. Moreover, the microstructure of GPC is significantly different from that of PC concrete microstructure and can be affected by many factors.

Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study; secondly, boosting models outperformed the bagging models.

Ground vibration is one of the most unfavourable environmental effects of blasting activities, which can cause serious damage to neighboring homes and structures. As a result, effective forecasting of their severity is critical to controlling and reducing their recurrence. There are several conventional vibration predictor equations available proposed by different researchers but most of them are based on only two parameters, i.e., explosive charge used per delay and distance between blast face to the monitoring point. It is a well-known fact that blasting results are influenced by a number of blast design parameters, such as burden, spacing, powder factor, etc. but these are not being considered in any of the available conventional predictors and due to that they show a high error in predicting blast vibrations. Nowadays, artificial intelligence has been widely used in blast engineering. Thus, three artificial intelligence approaches, namely Gaussian process regression (GPR), extreme learning machine (ELM) and backpropagation neural network (BPNN) were used in this study to estimate ground vibration caused by blasting in Shree Cement Ras Limestone Mine in India. To achieve that aim, 101 blasting datasets with powder factor, average depth, distance, spacing, burden, charge weight, and stemming length as input parameters were collected from the mine site. For comparison purposes, a simple multivariate regression analysis (MVRA) model as well as, a nonparametric regression-based technique known as multivariate adaptive regression splines (MARS) was also constructed using the same datasets. This study serves as a foundational study for the comparison of GPR, BPNN, ELM, MARS and MVRA to ascertain their respective predictive performances. Eighty-one (81) datasets representing 80% of the total blasting datasets were used to construct and train the various predictive models while 20 data samples (20%) were utilized for evaluating the predictive capabilities of the developed predictive models. Using the testing datasets, major indicators of performance, namely mean squared error (MSE), variance accounted for (VAF), correlation coefficient (R) and coefficient of determination (R2) were compared as statistical evaluators of model performance. This study revealed that the GPR model exhibited superior predictive capability in comparison to the MARS, BPNN, ELM and MVRA. The GPR model showed the highest VAF, R and R2 values of 99.1728%, 0.9985 and 0.9971 respectively and the lowest MSE of 0.0903. As a result, the blast engineer can employ GPR as an effective and appropriate method for forecasting blast-induced ground vibration.

Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority.

Determining the fracture toughness of rock materials is a challenging, costly, and time-consuming task, as fabricating a sharp crack in rock specimens will lead to failure of the specimen, and preparing specimens for determining the rock fracture toughness requires special equipment. In this paper, the relationship between mode I fracture toughness (KIC) with the rock index properties, mineralogy, and petrography of limestone is investigated using simple nonlinear and simple/multiple linear regression analyses to provide alternative methods for estimating the fracture toughness of limestones. The cracked chevron notched Brazilian disk (CCNBD) method was applied to 30 limestones with different petrographic and mineralogical characteristics under both dry and saturated conditions. Moreover, the index properties of the same rocks, including the density, porosity, electrical resistivity, P and S wave velocities, Schmidt rebound hardness, and point load index, were determined. According to the statistical analyses, a classification based on the petrography of the studied rocks was required for predicting the fracture toughness from index properties. By classifying the limestones based on petrography, reliable relationships with high correlations can be introduced for estimating the fracture toughness of different limestones using simple tests.

The safe disposal of an enormous amount of waste glass (WG) in several countries has become a severe environmental issue. In contrast, concrete production consumes a large amount of natural resources and contributes to environmental greenhouse gas emissions. It is widely known that many kinds of waste may be utilized rather than raw materials in the field of construction materials. However, for the wide use of waste in building construction, it is necessary to ensure that the characteristics of the resulting building materials are appropriate. Recycled glass waste is one of the most attractive waste materials that can be used to create sustainable concrete compounds. Therefore, researchers focus on the production of concrete and cement mortar by utilizing waste glass as an aggregate or as a pozzolanic material. In this article, the literature discussing the use of recycled glass waste in concrete as a partial or complete replacement for aggregates has been reviewed by focusing on the effect of recycled glass waste on the fresh and mechanical properties of concrete.

Applications related to alkali-activated materials (AAMs) have received much attention due to their excellent mechanical properties and low-energy production. Although much research has focused on developing AAMs, their application is still limited. One of the primary reasons is the efflorescence. Not only does efflorescence affect the material aesthetics, but it also affects the mechanical performance, leading to a decrease in material quality. This paper first summarizes the current research on AAMs efflorescence. The formation process of efflorescence is divided into three parts: alkaline cation leaching, air carbonation, and efflorescence formation. Furthermore, the influences caused by different factors, including raw materials, curing conditions, AAMs modalities, etc., on the efflorescence are proposed. This paper highlights the solutions for efflorescence by avoiding free alkaline cation leaching and preventing air carbonation. The advantages and disadvantages of efflorescence are discussed in-depth, showing that it can be exploited under certain conditions, such as in wastewater treatment. This paper has important implications for the practical preparation and application of AAMs.

SAC (sulfoaluminate cement) has become a research hotspot as a low-carbon ecological cement. In addition, multi-walled carbon nanotubes have good thermal, mechanical, and electrical properties and can serve as excellent nano-reinforced cement-based fillers. This study explored the dispersion of carbon nanotubes (CNTs) and researched the effect of CNTs on the mechanical properties, hydration process, hydration products, and microstructure of SAC paste, and the mechanism of CNT-enhanced SAC paste was revealed. The results showed that the mechanical properties of SAC paste were significantly improved after the addition of CNTs. When the CNT content was 0.05%, 0.1%, and 0.15%, the compressive strength after 28 d was increased by 13.2%, 18.3%, and 22.5%, respectively; compared with the C0 group (without CNTs), the flexural strength increased by 8.2%, 11.3%, and 14.4%, respectively. The addition of CNTs accelerated the hydration process of SAC paste. Due to the adsorption effect and nucleation effect of CNTs, more hydration products were generated, filling the matrix’s pores and improving its compactness. The mechanism of CNTs enhanced SAC paste was revealed. CNTs and hydration products co-filled the pores, including AFt (ettringite) and AH3 (gibbsite). CNTs improve the mechanical properties of SAC paste through filling, bridging, crack bending, deflection, pulling out, and pulling off.

Prediction of pile bearing capacity has been considered an unsolved problem for years. This study presents a practical solution for the preparation and maximization of pile bearing capacity, considering the effects of time after the end of pile driving. The prediction phase proposes an intelligent equation using a genetic programming (GP) model. Thus, pile geometry, soil properties, initial pile capacity, and time after the end of driving were considered predictors to predict pile bearing capacity. The developed GP equation provided an acceptable level of accuracy in estimating pile bearing capacity. In the optimization phase, the developed GP equation was used as input in two powerful optimization algorithms, namely, the artificial bee colony (ABC) and the grey wolf optimization (GWO), in order to obtain the highest bearing capacity of the pile, which corresponds to the optimum values for input parameters. Among these two algorithms, GWO obtained a higher value for pile capacity compared to the ABC algorithm. The introduced models and their modeling procedure in this study can be used to predict the ultimate capacity of piles in such projects.

In deep foundations, the pile group and the pile raft are generally used. To date, the contribution of the raft is not taken into account in the design, even when the raft is in contact with the soil and the whole system is therefore considered to work as a pile group foundation. In a combined pile raft system, the raft takes a considerable portion of the applied load, depending upon the number of piles, the spacing to diameter ratio of the piles, and the length to diameter ratio. In this paper, an experimental investigation is carried out to study the response of small-scale pile group and piled raft models with a varying number of piles subjected to both vertical and lateral loads. Additionally, the response mechanism of these models to both types of loads is also studied. A comparison was made between these models. It was found that, unlike the pile group, the piled raft provides considerably high stiffness to both types of loads, and the difference between the stiffness of both systems decreases as the number of piles increases. By comparing the response of the piled raft and the pile group with the same number of piles under the same vertical and lateral load, it was concluded that the piled raft response to the lateral and vertical loads was much stiffer than the pile group response. The lateral deflection and the vertical settlement of the piled raft were less than those of the pile group with the same pile configuration. This effective response of the piled raft to the vertical and lateral loads was due to the raft contribution in resisting the vertical and lateral loads. Moreover, with the increase in the number of piles, the vertical and lateral contribution of the raft decreases.

This research is aimed at investigating the mechanical behavior of the bitumen by the addition of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) obtained from waste plastic bottles and bags. Polymers (HDPE and LDPE) with percentages of 0%, 2%, 4%, and 6% in shredded form by weight of bitumen were used to evaluate the spectroscopic, structural, morphological, and rheological properties of polymer-modified binders. The rheological properties for different factors; viscosity (ἠ) from Rotational Viscometer (RV), rutting factor G*/Sin (δ), fatigue characteristics G*. Sin (δ), for the modified binder from dynamic shear rheometer (DSR), Short and long-term aging from rolling thin film oven (RTFO), and pressure aging vessel (PAV) was determined. The thermal characteristics, grain size, and texture of polymers for both LDPE and HDPE were found using bending beam rheometer (BBR) and X-ray diffraction (XRD), respectively. Fourier transform infrared (FTIR) analysis revealed the presence of polymer contents in the modified binder. Scanning electron microscopy (SEM) images revealed the presence of HDPE and LDPE particles on the surface of the binder. Creep Rate (m) and Stiffness (S) analysis in relationship with temperature showed a deduction in stress rate relaxation. Results have revealed the best rutting resistance for 6% HDPE. It also showed an improvement of 95.27% in G*/Sin (δ) which increased the performance of the bituminous mix. Similarly, the addition of 4% LDPE resulted in maximum dynamic viscosity irrespective of the temperatures. Moreover, fatigue resistance has shown a significant change with the HDPE and LDPE. The festinating features of waste plastic modified binder make it important to be used in the new construction of roads to address the high viscosity and mixing problems produced by plastic waste and to improve the performance of flexible pavements all over the world.

Shear walls have high strength and stiffness, which could be used at the same time to resist large horizontal loads and weight loads, making them pretty beneficial in several structural engineering applications. The shear walls could be included with openings, such as doors and windows, for relevant functional requirements. In the current study, a building of G + 13 stories with RC shear walls with and without openings has been investigated using ETABS Software. The seismic analysis is carried out for the determination of parameters like shear forces, drift, base shear, and story displacement for numerous models. The regular and staggered openings of the shear wall have been considered variables in the models. The dynamic analysis is carried out with the help of ETABS software. It has been observed that shear walls without openings models perform better than other models, and this is in agreement with the previous studies published in this area. This investigation also shows that the seismic behaviour of the shear wall with regular openings provides a close result to the shear wall with staggered openings. At the roof, the displacement of the model with regular openings was 38.99 mm and approximately 39.163 mm for the model with staggered openings. However, the model without a shear wall experienced a displacement of about 56 mm at the roof. Generally, it can be concluded that the openings have a substantial effect on the seismic behaviour of the shear wall, and that should be taken into consideration during the construction design. However, the type of opening (regular or staggered) has a slight effect on the behaviour of shear walls.

Sulfoaluminate cement (SAC) is a low carbon ecological cement with good durability and is widely used in various projects. In addition, graphene nanoplates (GNPs) have excellent thermal, electrical, and mechanical properties and are excellent nano-filler. However, the hydration behavior of GNPs on SAC is still unclear. In this paper, the effect of GNPs on SAC hydration was investigated by isothermal calorimetry, and the hydration kinetic model and hydration kinetic equation of SAC was established, explaining the differences in cement hydration processes with and without GNPs on SAC based on a hydration kinetic model. Results indicate that the hydration exotherm of SAC mainly includes five stages: the initial stage, the induction stage, the acceleration stage, the deceleration stage, and the stable stage. The addition of GNPs promoted the hydration exotherm of SAC and accelerated the hydration reaction. Different from the hydration reaction of Portland cement, the hydration reaction of SAC is mainly a diffusion–reaction process.

Low-carbon ecological cement composites are among the most promising construction materials. With low energy consumption, low carbon dioxide emissions, and high early strength, sulfoaluminate cement (SAC) is a low-carbon ecological building material. In addition, graphene nanoplates (GNPs) exhibit excellent performances. In this study, GNPs were dispersed by a combination of dispersant and ultrasonic treatment, and the dispersion effect of GNPs was characterized. The effect of GNPs on the hydration process and products of SAC was studied, revealing that GNPs accelerate SAC hydration. The hydration heat and ICP results showed that in the SAC hydrolysis stage, C4A3Š (ye’elimite) hydrolyzed and released Ca2+. GNPs absorbed the Ca2+, and the Ca2+ concentration around C4A3Š decreased, which would promote the hydrolysis of C4A3Š and release more Ca2+, accelerating the hydration of SAC and the nucleation effect of GNPs, and providing sites for the formation of hydration products. The analysis of XRD (X-Ray Diffraction) and TGA (Thermal Gravity Analysis) showed that GNPs promoted the hydration of SAC and formed more AFt (ettringite) and AH3 (gibbsite). The generated hydration products fill the pores of the matrix and are closely connected to the GNPs to form a whole, which improves the cement matrix’s mechanical properties.

As a low-carbon ecological cement-based material, SAC (sulfoaluminate cement) has become a research hotspot. This study developed a SAC-based high-performance concrete material with good durability and high toughness. The mechanical properties of different scales of MSF (macro steel fiber) and mSF (micro steel fiber) reinforced sulfoaluminate cement-based composites were mainly studied, including their compressive strength, flexural strength, toughness index, and toughness ratio, and their resistance to sulfate erosion was characterized. The results show that adding MSF and HSF (hybrid steel fibers) can significantly improve concrete’s compressive and flexural strength compared with the Plain group. The compressive strength of SSF1 (1% MSF) and SSF2 (1.5% MSF) increased by 10.9%, 19.6%, and the compressive strength of HSF1 (0.1% mSF, 1.4% MSF), HSF2 (0.2% mSF, 1.3%MSF), HSF3 (0.3% mSF, 1.2% MSF), and HSF4 (0.5% mSF, 1.0% MSF) increased by 23.9%, 33.7%, 37.0%, 29.3%, respectively, while the flexural strength of HSF1, HSF2, HSF3, and HSF4 groups increased by 51.4%, 84.9%, 88.1%, and 64.2%. Compared with the single steel fiber (SSF) group, the HSF group has higher initial crack strength, equivalent flexural strength, toughness index, and toughness ratio. Hybrid fibers have a higher synergistic effect when mSF content is 0.2–0.3% and MSF content is 1.2–1.3%. The mechanism of multi-scale reinforcement of hybrid-steel-fiber-enhanced sulfoaluminate cement-based composites was researched. MSF bridges macro-cracks, mSF bridges micro-cracks, and these two different scales of steel fibers, through filling, bridging, anchoring, pulling off, and pulling out, improve the toughness of composite materials. The mechanism of sulfate corrosion resistance of sulfoaluminate cement-based composites was obtained. SO42− entered the matrix and reacted and formed AFt, filling the matrix’s pores. The whole process is similar to the self-healing process of concrete.

The international roughness index (IRI) can be employed to evaluate the smoothness of pavement. The previously proposed mechanical-empirical pavement design guide (MEPDG), which is used to model the IRI of joint plain concrete pavement (JPCP), has been modified in this study considering its disadvantage of low prediction accuracy. To improve the reliability of the prediction effect of the IRI for JPCP, this study compares the prediction accuracy of the IRI of JPCP by using the machine-learning methods of support vector machine (SVM), decision tree (DT), and random forest (RF), optimized by the hyperparameter of the beetle antennae search (BAS) algorithm. The results from the machine-learning process show that the BAS algorithm can effectively improve the effectiveness of hyperparameter tuning, and then improve the speed and accuracy of optimization. The RF model proved to be the one with the highest prediction accuracy among the above three models. Finally, this study analyzes the importance score of input variables to the IRI, and the results show that the IRI was proportional to all the input variables in this study, and the importance score of initial smoothness (IRII) and total joint faulting cumulated per km (TFAULT) were the highest for the IRI of JPCP.