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Abstract Laterite is a naturally occurring rock formation that is used worldwide as a building material in tropical regions. Its unique features have enabled some of the most astounding architectural feats, which irrevocably altered the cultural and historical contexts of their surroundings. This study examines the laterite stone masonry performance, with a specific focus considering its mechanical properties, mineral composition, and microstructure. However, mineralogical composition and microstructural features vary, affecting mechanical qualities and endurance. The study also extensively examines four varieties of laterite samples used for construction in the Udupi region. Experiments were performed to determine the percentage of water absorption and its impact on the compressive strength of Laterite stone. L1 Laterite stone showed better performance due to its exceptional strength. It surpasses the other samples and is well-suited for the construction of load-bearing structures as it aligns with the IS 3620 standards. Stack bond masonry prism samples were built using four varieties of laterite stones with 1:6 cement mortar for the joints. These prism specimens were subsequently tested under compression, tension, and shear loading to determine the variation in their behaviour. The series of tests performed revealed that the prisms failed due to splitting and crushing under compression loads. In shear and tensile bond tests, all laterite masonry prism samples failed due to rupture of the joint. Microstructure study confirmed that the increased strength of lateritic samples L1 and L2 is due to less prominent clay particles coupled with a higher percentage of ferrous content.

Abstract Road traffic crashes (RTCs) are considered one of the major public health issues in many countries worldwide. Investigating factors of traffic crashes, accidents, and disasters can facilitate and aid in identifying measures to mitigate their frequency and severity as well as occurrence and impact, thereby enhancing road safety. This study aims to investigate the factors that contribute to road traffic accidents in the Gaza Strip, Palestine. A comprehensive questionnaire, comprising four sub-groups of factors: human, vehicle, road, and environmental, collected data from a sample of respondents, including drivers, passengers, pedestrians, and other road users. The study utilized the relative importance index (RII) to analyze and classify each factor group after analyzing 385 questionnaires in SPSS. Over 83% of respondents across all age categories identified overspending as the most significant factor causing road traffic crashes, according to the study’s findings. Additionally, more than 78% of respondents believed that weak braking or brake failure was the highest and most significant vehicle-related factor contributing to road traffic crashes. Among the road factors, 75.4% of respondents identified low or no street illumination as a significant contributor to road traffic crashes, ranking it as the fourth most influential element overall, while environmental factors had the least impact compared to other variables.

Abstract Concrete’s widespread use in construction is increasing due to urbanization and population growth. While it offers durability and strength, its production poses environmental challenges. Researchers and global initiatives are working towards more sustainable concrete production methods and effective waste management, particularly for paper waste (WPA) and polyethylene terephthalate waste (PET). This study aims to investigate the effects of waste paper aggregate (WPA) on the flow time, compressive strength, flexural strength, and thermal conductivity of mortars. Furthermore, it explores the potential of polyethylene terephthalate (PET) to enhance the compressive and flexural strengths of these mortars. To conduct this study, the materials were characterized using EDX spectroscopy and SEM analysis. Gwyddion software was employed to conduct a comprehensive analysis of the grain segmentation and microstructural characteristics of the WPA mortars. Subsequently, four distinct mixtures were selected for further investigation. The first mixture involved substituting sand with various percentages of waste paper aggregate (WPA) (1–4%) without PET. The second mixture included WPA (1–4%) and 5% PET. Due to the ineffective plasticizer in the first two mixtures, the third mixture replaced 20% of quarry sand with dune sand (DS) while maintaining the same percentages of WPA and PET as in the previous mixtures. The results indicate that mortars RM1 (0% WPA + 0% PET) and RM2 (0% WPA + 0% PET + 20% DS) demonstrated the best flow time. This suggests that incorporating WPA and PET into the mortar mix can negatively impact its workability, making it more challenging to handle, shape, and apply. Sample PW4, composed of 4% WPA and 0% PET, displayed a significant increase in compressive strength from 46.36 MPa to 49.81 MPa and flexural strength from 10.5 MPa to 11.47 MPa, indicating that incorporating WPA into the mortars can enhance resistance to both compressive and flexural strengths. This same mixture, PW4, demonstrated that the mortars are poor conductors of heat and would be more effective as thermal insulators. Grain segmentation of WPA mortars using Gwyddion software revealed that shorter WPA fibers tend to align with the width, while longer WPA fibers tend to align with the length of the section.

Abstract This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns. These techniques include Support Vector Regressor (SVR), Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), MLP Regressor (MLPR), K-nearest Neighbours Regressor (KNNR), and Naive Bayes Regressor (NBR). ML models accuracy is assessed by comparing their predictions with FE results. Among the models, GBR and XGBR exhibited outstanding results with high test R2 scores of 0.9888 and 0.9885, respectively. The study provided insights into the contributions of individual features to predictions using the SHapley Additive exPlanations (SHAP) approach. The results from SHAP indicate that the eccentric loading ratio (e/2a) has the most significant effect on the load-carrying capacity of elliptical CFST short columns, followed by the yield strength of the outer steel tube ( $$\:{f}_{yo}$$ ) and the inner width of the inner steel tube ( $$\:2{a}_{ii}$$ ). Additionally, a user interface platform has been developed to streamline the practical application of the proposed ML.

Abstract This study investigated the chemical properties of peat microparticles modified asphalt (Pt.M.A.). The originality of the study resides in the examination of the chemical characteristics of peat microparticles (Pt.) modified asphalt (Pt. M.A.) utilising FTIR, SEM, SFE, and XRD methodologies. This encompasses Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), surface free energy (SFE), and X-ray diffraction (XRD). Initially, FTIR examined the functional groups of both unaltered and altered asphalt binders. The SEM images reveal improved compatibility, showcasing superior diffusion of the modifier across the asphalt. A further critical factor is that improved adhesion properties, according to the SFE study, indicate that modified binders generally offer more SFE compared to unmodified binders. The XRD measurements revealed a semi-crystalline structure in the Pt. modifier and an amorphous structure in the basal asphalt binder. The integration of Pt. into the asphalt cement resulted in modifications to the phases of both constituents, culminating in the emergence of a new semi-crystalline phase inside the modified asphalt binder. These data suggest that peat microparticles (Pt.) can improve the efficacy of asphalt binders by enhancing compatibility, adhesion, and resistance to ageing.

Abstract This article investigates the behavior of hybrid FRP Concrete-Steel columns with an elliptical cross section. The investigation was carried out by gathering information through literature and conducting a parametric study, which resulted in 116 data points. Moreover, multiple machine learning predictive models were developed to accurately estimate the confined ultimate strain and the ultimate load of confined concrete at the rupture of FRP tube. Decision Tree (DT), Random Forest (RF), Adaptive Boosting (ADAB), Categorical Boosting (CATB), and eXtreme Gradient Boosting (XGB) machine learning techniques were utilized for the proposed models. Finally, these models were visually and quantitatively verified and evaluated. It was concluded that the CATB and XGB are standout models, offering high accuracy and strong generalization capabilities. The CATB model is slightly superior due to its consistently lower error rates during testing, indicating it is the best model for this dataset when considering both accuracy and robustness against overfitting.

Abstract The dynamic analysis of municipal solid waste (MSW) is essential for optimizing landfills and advancing sustainable development goals. Assessing damping ratio (D), a critical dynamic parameter, under laboratory conditions is costly and time-consuming, requiring specialized equipment and expertise. To streamline this process, this research leveraged several novel ensemble machine learning models integrated with the equilibrium optimizer algorithm (EOA) for the predictive analysis of damping characteristics. Data were gathered from 153 cyclic triaxial experiments on MSW, which examined the age, shear strain, weight, frequency, and percentage of plastic content. Analysis of a correlation heatmap indicated a significant dependence of D on shear strain within the collected MSW data. Subsequently, five advanced machine learning methods—adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), random forest (RF), and cubist regression—were employed to model D in landfill structures. Among these, the GBRT-EOA model demonstrated superior performance, with a coefficient of determination (R2) of 0.898, root mean square error of 1.659, mean absolute error of 1.194, mean absolute percentage error of 0.095, and an a20-index of 0.891 for the test data. A Shapley additive explanation analysis was conducted to validate these models further, revealing the relative contributions of each studied variable to the predicted D-MSW. This holistic approach not only enhances the understanding of MSW dynamics but also aids in the efficient design and management of landfill systems.

Abstract Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching–learning based optimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, $${VA}_{orig}$$ VA orig (%); mean annual air temperature, $$MAAT$$ MAAT (°F); original viscosity at 77 °F, $${\eta }_{orig,77}$$ η o r i g , 77 (Mega-Poises); and $$time$$ time (months). EPR-TLBO was found to be superior to MGGP and existing empirical models due to the interquartile ranges of absolute error boxes equal to 0.67%. EPR-TLBO had an R2 value of more than 0.90 in both the training and testing phases, and only less than 20% of the records were predicted utilizing this model with more than 20% deviation from the observed values. As determined by the sensitivity analysis, $${\eta }_{orig,77}$$ η o r i g , 77 is the most significant of the four input variables, while time is the least one. A parametric study showed that regardless of $$MAAT$$ MAAT , $${\eta }_{orig,77},$$ η o r i g , 77 , of 0.3 Mega-Poises, and $${VA}_{orig}$$ VA orig above 6% can be ideal for improving the pavement service life. It was also witnessed that with an increase of $$MAAT$$ MAAT from 37 to 75 °F, the serviceability of asphalt concrete takes 15 months less on average.

There is still insufficient data on the behavior of tubed-reinforced concrete columns (TRCCs) under the eccentric compression. Thus, this research work comprehensively examines the eccentric compression behavior of TRCCs using nonlinear finite element modeling and machine learning (ML). To do this, numerical simulation and parametric analysis based on existing investigations were conducted. In addition to the existing 22 specimens with limited test variables, additional 188 specimens were developed to cover a wide range of parameters, including the load eccentricity, transverse reinforcement spacing, columns’ slenderness ratio, yield strength of steel, and outer steel tube diameter. Additionally, six ML models were created to estimate the ultimate load results. The results indicated that increasing the outer steel tube yield strength and diameter, and reducing the load eccentricity, slenderness ratio, and spacing of the transverse reinforcement enhanced the load-carrying capacity of the columns. The Gaussian process regression model demonstrated superior performance metrics in comparison to other ML models, with the highest R2 values (0.998613 in training and 0.99823 in testing stages) and lowest root mean square error values (0.007213 in training and 0.008471 in testing stages). To save money, time, and resources compared to laboratory testing, an online-based prediction program is finally presented to predict the columns’ ultimate load.

Three different densities (500 kg/m3, 1000 kg/m3, and 1500 kg/m3) of foamed concrete (FC) were tested alongside mortar with a density of 1980 kg/m3 to investigate how high temperatures affect the qualities of FC. A flow table test was used to examine the fresh qualities of the mixtures. The modulus of elasticity, ultrasonic pulse velocity (UPV), bending strength, split tensile strength, compressive strength, thermal conductivity, porosity, and appearance and colour changes at ambient temperature and after exposure to various high temperatures (100 °C, 150 °C, 200 °C, 400 °C, 600 °C, and 800 °C) were evaluated. To study the effects of varying densities, microstructure analysis was performed utilizing scanning electron microscopy and mercury intrusion porosimetry. According to the findings, the four varied densities appeared dissimilar. FC with lower densities (500 kg/m3 and 1000 kg/m3) showed signs of cracking, while FC with a higher density (1500 kg/m3) enabled for precise detection of the pore connectivity and surface spalling occurrences. High temperatures had less effect on the mortar than FC mixtures. As the temperature increased, the modulus of elasticity, split tensile strength, bending strength, compressive strength, thermal conductivity, and mass loss decreased for all the mortar and FC samples. The UPV values increased marginally up to 100 °C before decreasing. This investigation highlighted the need for additional research and code provisions that consider different innovative construction materials and FC constituent classes.

This study investigates the structural behaviour of double-skin columns, introducing novel double-skin double filled tubular (DSDFT) columns, which utilise double steel tubes and concrete to enhance the load-carrying capacity and ductility beyond conventional double-skin hollow tubular (DSHT) columns, employing a combination of finite element model (FEM) and machine learning (ML) techniques. A total of 48 columns (DSHT+DSDFT) were created to examine the impact of various parameters, such as double steel tube configurations, thickness of fibre-reinforced polymer (FRP) layer, type of FRP material, and steel tube diameter, on the load-carrying capacity and ductility of the columns. The results were validated against the experimental findings to ensure their accuracy. Key findings highlight the advantages of the DSDFT configuration. Compared to the DSHT columns, the DSDFT columns exhibited remarkable 19.54 % to 101.21 % increases in the load-carrying capacity, demonstrating improved ductility and load-bearing capabilities. Thicker FRP layers enhanced the load-carrying capacity up to 15 %, however at the expense of the reduced axial strain. It was also observed that glass FRP wrapping displayed 25 % superior ultimate axial strain than aramid FRP wrapping. Four different ML models were assessed to predict the axial load-carrying capacity of the columns, with long short-term memory (LSTM) and bidirectional LSTM models emerging as superior choices indicating exceptional predictive capabilities. This interdisciplinary approach offers valuable insights into designing and optimising confined column systems. It sheds light on both double-tube and single-tube configurations, propelling advancements in structural engineering practices for new constructions and retrofitting. Further, it lays out a blueprint for maximising the performance of the confined columns under the axial compression.

Nowadays, due to the structural advantages gained by combining three different materials' properties, columns made of carbon-fiber reinforced polymer (CFRP)-confined concrete with inner steel tube have received researchers' interest. This article presents the nonlinear finite element analysis and multiple machine learning (ML) model-based study on the behavior of round corner rectangular CFRP-confined concrete short columns reinforced by the inner high-strength elliptical steel tube under the axial load. The reliability of the proposed nonlinear finite element model was verified against the existing experimental investigations. The effects of the parameters such as the concrete grade, thickness of reinforcing steel tube, cross-sectional size of inner steel tube, and thickness of CFRP on the behavior of the columns are comprehended in this study. Furthermore, multiple ML models were proposed to predict the ultimate axial load, ultimate axial strain, and lateral strain of the test specimens. The reliability of the proposed ML models was evaluated by six distinct performance metrics. From the parametric investigation, it was found that concrete with lower compressive strength gained more strength enhancement because of confinement between CFRP and the inner steel tube than high-strength concrete relative to its unconfined compressive strength. The proposed ML models of extreme gradient boosting and random forest provided the best-fit results than the artificial neural network and Gaussian process regression models in predicting the axial load and axial and lateral strains of the columns.

The production of eco-friendly concrete has been made possible by reusing agricultural and industrial wastes. This paper presents an experimental investigation of the characteristics of lightweight foamed concrete (LWF) produced from a protein-based foaming agent and including granulated blast furnace slag (GGBS), fly ash (FA), rice husk ash (RHA) and palm oil fuel ash (POFA) at various substitution levels (0, 10 %, 20 %, 30 %, 40 %, 50 %, and 60 %) with cement. By executing a slump test, the fresh characteristics of mixes were assessed. In addition, a total of 25 different LWF mixtures were produced and tested for their porosity, bulk density, compressive strength, bending strength, splitting tensile strength, water absorption, ultrasonic pulse velocity (UPV), and thermal conductivity. To elucidate the causes for the experimental findings acquired, microstructural analysis was also performed. The findings indicate that the GGBS, FA, RHA, and POFA ratios of the LWFs increased due to a reduction in slump, porosity, water absorption, bulk density, and thermal conductivity up to 40 % GGBS, 30 % FA, 20 % RHA, and 30 % POFA. However, the compressive strength, bending strength, splitting tensile strength, UPV were raised up to 40 % GGBS, 30 % FA, 20 % RHA and 30 % POFA as substitution for cement. LWF containing 40 % GBS as a cement substitution also demonstrate larger compressive strength, bending strength, splitting tensile strength, and ultrasonic pulse velocity in comparison with the control, 30 % FA, 20 % RHA, and 30 % POFA LWF. The findings are promising and reveal a major opportunity for developing eco-friendly LWF by partially substituting cement with GGBS and FA industrial by-product material, RHA and POFA agricultural waste materials as well.

Local buckling of steel and excessive spalling of concrete have necessitated the need for the evaluation of reinforced concrete columns subjected to axial compression loading. Thus, this study investigates the behaviour of concrete filled steel tube (CFST) columns and reinforced concrete filled steel tube (RCFST) columns under the axial compression using the finite element modelling and machine learning (ML) techniques. To achieve this aim, a total of 85 columns from existing studies were analysed utilising the finite element modelling. The ultimate load of the generated datasets was predicted employing various ML techniques. The findings showed that the columns' compressive strength, ductility, and toughness were improved by reducing transverse reinforcement spacing, increasing the number of reinforcing bars, and increasing the thickness and yield strength of outer steel tube. Under the axial compression loading, the finite element modelling analysis provided an accurate assessment of the structural performance of the RCFST columns. Compared to other ML approaches, gradient boosting exhibited the best performance metrics with R2 and root mean square error values of 99.925% and 0.00708 and 99.863% and 0.00717 respectively in training and testing stages, to predict the columns' ultimate load. Overall, gradient boosting can be applied in the ultimate load prediction of CFST and RCFST columns under the axial compression, conserving resources, time, and cost in the investigation of the ultimate load of columns through laboratory testing.

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.

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.

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.

As an environmentally friendly material, geopolymers are demanded to have good mechanical and fracture strength. Therefore, this research studied the mechanical properties and fracture parameters of geopolymers based on cellulose nanocrystals (CNCs) from typha sp. fibers. The different percentages of CNCs studied were 0%, 1%, 2% and 3%. Various mechanical parameters were evaluated, such as compressive strength, flexural strength, fracture toughness, and direct tensile strength. Microstructures were also evaluated to strengthen the assessment, which included Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The results showed that using 1% CNCs was the most effective for improving the mechanical properties, fracture behavior, and microstructural of CNCs-based geopolymers. Using 1% CNCs in geopolymer showed maximum increases of 15.76% and 11.72%, respectively, for compressive strength and tensile strength. Meanwhile, using 1% of CNCs improved flexural strength and fracture toughness up to 3.36 times. The addition of more than 1% CNCs even decreased the mechanical properties of CNCs-based geopolymers. SEM also confirmed that the inclusion of 1% CNCs increased the microstructure and produced a denser structure, and FTIR showed the presence of Si–O–Al bonds. CNCs-based geopolymers from typha sp. fibers can improve mechanical properties and fracture parameters, thus being suitable for reinforcement material and feasible to apply to construction.

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.

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.

The production of cement results in the emission of much carbon dioxide, which contributes to ‎ ‎undesirable environmental impacts such as climate change and global warming. These ‎phenomena ‎have rekindled interest in utilizing a variety of industrial waste products to ‎produce geopolymer ‎‎(GP) composites and alkali-activated (AA) binders in order to reduce the ‎usage of ordinary ‎Portland cement in building construction. Waste red mud (RM), also known as bauxite residue, is one of these ‎hazardous radioactive ‎waste materials that is formed as a by-product of Bayer's aluminum ‎manufacturing process. This ‎paper conducts a systematic review of the literature on the use of ‎RM and slag in the production ‎of red mud-slag geopolymer (RM-SGP). An overview of the ‎economic and environmental ‎impacts, physical and chemical properties, production, ‎distribution, classification, and potential applications ‎of RM are presented. ‎Besides, recent advancements in the usage of RM and slag for geopolymer ‎production are ‎described in terms of physical, mechanical, durability, and microstructure ‎ properties. Moreover, this ‎study attempts to chart a route toward a realistic valorization that ‎reflects both real and perceived ‎concerns, such as radioactivity, leaching, and the life cycle ‎assessment of red mud geopolymer (RM-GP). The potential ‎use of RM-SGP production indicates the need for further studies into ‎the mixture proportion ‎and combination of these two raw ingredients with other cementitious materials ‎leading to new ‎energy-saving and affordable building products and processes. Also, it is ‎recommended that ‎research efforts be directed ‎toward economic, life cycle, and environmental ‎assessments.‎

The use of fiber-reinforced polymer (FRP) bars to overcome the corrosion problems in various reinforced concrete structures is now well documented in the literature. As a result, the currently available design guidelines such as North American design codes allow for using the FRP bars as alternative materials to steel bars to be incorporated into the concrete structures. In practice, hollow-core concrete columns (HCCs) are widely accepted to make a lightweight structure and reduce its cost. Due to the lack of laboratory tests, engineers may not perform a safe design of HCCs with internal FRP bars. Therefore, the presented paper has endeavored to numerically and theoretically explore using the FRP bars and spirals as internal reinforcement for HCCs and investigate the effects of several test parameters. Using the current version of Finite Element Analysis (FEA) ABAQUS (3DS, 2014), a total of 116 HCCs were simulated based on 29 specimens experimentally tested by the researchers which acted as control specimens for the FE model. The complex structural response of concrete was reasonably determined using the concrete damaged plasticity model (CDPM) and the mechanical response of the FRP rebars are considered to behave linearly up to failure with no yielding stage. The calibrated FE model can provide an excellent portrayal of the HCCs’ response. Based on the database obtained from laboratory and simulation, several Artificial Neural Network (ANN) models were further provided to predict the confined compressive load of GFRP-RC HCCs at different loading stages.

In recent decades, several studies have considered the use of plastic waste as a partial substitute for aggregate in green concrete. Such concrete has been limited to non-structural applications due to its low strength. This raises whether such concrete can be enhanced for use in some structural applications. This paper reports an attempt to develop a structural-grade concrete containing plastic waste aggregate with high proportions of substitution and confined with carbon fiber reinforced polymer (CFRP) fabrics. Experimental research was conducted involving the casting and testing 54 plain and confined concrete cylinders. A concrete mixture was designed in which the fine aggregate was partially replaced by polyethylene terephthalate (PET) waste plastic at ratios of 0%, 25%, and 50%, and with different w/c ratios of 0.40, 0.45, and 0.55. The results show that confinement has a substantial positive effect on the compressive behavior of PET concrete. The enhancement efficiency increases by 8–190%, with higher enhancement levels for higher substitution ratios. Adding one layer of CFRP fabric raises the ultimate strength of samples that have lost compressive strength to a level close to that of unconfined samples not containing PET. This confinement is accompanied by an increase in the slope of the stress-strain curve and greater axial and lateral strain values at failure. For the specimens confined by CFRP fabric, PET aggregate can be used as a partial substitute for sand at a replacement ratio of up to 50% by volume for structural applications. This paper also considers the ability of existing models to predict the strength of confined-PET concrete circular cross-sections by comparing model predictions with experimental results. The strength of confined PET concrete elements can’t be accurately predicted by any of the models that are already out there. It’s important to come up with a new model for these elements.

The environmental aspects of sustainable development in the construction industry consist of the utilization of secondary raw materials and materials which can be recycled in the design and construction of new structures. The preliminary and inevitable interest in the use of full or partial replacements of by-products as complementary pozzolanic materials was mostly induced by the enforcement of the reduction/elimination of the greenhouse gas emission from the production of Portland cement. With the significant evolution of geopolymer concrete as an alternative for Portland cement in the past decade, it is necessary to explore possible construction applications in which geopolymer concrete can be utilized. Hence, this review paper was carried out to explore various elements such as the precursors used in geopolymers concrete and their corresponding applications. The environmental impacts of various geopolymer concrete are also discussed. This paper also presents an overview of the real applications of geopolymer concrete for the construction of various infrastructures. Recommendations and prospects for geopolymer concrete are also provided.

Plastics have become an essential part of our daily lives, and global plastic production has increased dramatically in the past 50 years. This has significantly increased the amount of plastic garbage produced. Researchers have recently been interested in using trash and recyclable plastics in concrete as an ecologically acceptable building material. A large number of publications have been published that describe the behavior of concrete, containing waste and recovered plastic com ponents. However, information is scattered, and no one knows how plastic trash behaves as concrete materials. This research examines the use of plastic waste (PW) as aggregate or fiber in cement mortar and concrete manufacturing. The article reviewed the three most significant features of concrete: fresh properties, mechanical strength, and durability. PW and cement connections were also studied using microstructure analysis (scan electronic microscopy). The results showed that PW, as a fiber, enhanced mechanical performance, but PW, as a coarse aggregate, impaired concrete performance owing to poor bonding. The assessment also identified research needs in order to enhance the performance of PW-based concrete in the future.

In this paper, a seismic hazard assessment (SHA) of the Shigo Kas hydropower project has been performed by deterministic and probabilistic approaches. The previously developed MATLAB-based code has been used for deterministic SHA, incorporating local site effects through deep soil analysis. On the other hand, for probabilistic SHA, CRISIS 2007 has been used through diffuse areal source zones. The latest updated earthquake instrumental and historical catalogs have been developed. Based on the recommendations of the International Commission on Large Dams, peak ground acceleration (PGA) values for the maximum credible earthquake (MCE), safety evaluation earthquake (SEE), design basis earthquake (DBE) and operating basis earthquake (OBE) have been assessed, which are 0.50 g, 0.68 g, 0.35 g and 0.24 g, respectively, at the intake location, and 0.50 g, 0.61 g, 0.30 g and 0.22 g, respectively, at the powerhouse location. Hazard maps have been developed for scenario-based earthquakes (MCE) and for the peak ground acceleration of 145-, 475- and 2500-year return periods. The de-aggregation process has evaluated the combined effects of magnitude and distance. At a distance of 30 to 70 km from the earthquake source, earthquakes of magnitude 5 Mw to 5.6 Mw and 5.9 Mw to 6.5 Mw are more hazardous for the current project.