• Computers and Concrete
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• v.24 no.3
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• pp.249-258
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• 2019

In order to investigate the strength recovery of fire-damaged concrete after post-fire curing, concrete specimens were heating at $2^{\circ}C/min$ or $5^{\circ}C/min$ to 400, 600 and $800^{\circ}C$, and these exposed specimens were soaked in the water for 24 hours and following by 29-day post-fire curing. The compressive strength and split tensile strength of the high-temperature-exposed specimens before and after post-fire curing were tested. The proportion of split aggregate in the split surfaces was analyzed to evaluate the mortar-aggregate interfacial strength. After the post-fire curing process, the split tensile strength of specimens exposed to all temperatures was recovered significantly, while the recovery of compressive strength was only obvious within the specimens exposed to $600^{\circ}C$. The tensile strength is more sensitive to the mortar-aggregate interfacial cracks, which caused that the split tensile strength decreased more after high-temperature exposure and recovery more after post-fire curing than the compressive strength. The mortar-aggregate interfacial strength also showed remarkable recovery after post-fire curing, and it contributed to the recovery of split tensile strength.

• Korean Journal of Environmental Agriculture
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• v.38 no.1
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• pp.1-9
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• 2019

BACKGROUND: Accurate and simple analytical method determining Fluxametamid residue was necessary in various food matrices. Additionally, fulfilment of the international guideline of Codex (Codex Alimentarius Commission CAC/GL 40) was required for the analytical method. In this study, we developed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) method to determine the Fluxametamid residue in foods. METHODS AND RESULTS: Fluxametamid was extracted with acetonitrile, partitioned and concentrated with dichloromethane. To remove the interferences, silica SPE cartridge was used before LC-MS/MS (Liquid Chromatography-Tandem Mass Spectrometry) analysis with $C_{18}$ column. Five agricultural commodities (mandarin, potato, soybean, hulled rice, and red pepper) were used as a group representative to verify the method. The liner matrix-matched calibration curves were confirmed with coefficient of determination ($r^2$) greater than 0.99 at calibration range of 0.001-0.25 mg/kg. The limits of detection and quantification were 0.001 and 0.005 mg/kg, respectively. Mean average accuracies were shown to be 82.24-115.27%. The precision was also shown to be less than 10% for all five samples. CONCLUSION: The method investigated in this study was suitable to the Codex guideline for the residue analysis. Thus, this method can be useful for determining the residue in various food matrices as routine analysis.

• Computers and Concrete
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• v.29 no.1
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• pp.15-29
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• 2022

Concrete-filled steel tubes (CFSTs) are increasingly used as composite sections in structures owing to their excellent load bearing capacity. Therefore, predicting the mechanical behavior of CFST sections under axial compression loading is vital for design purposes. This paper presents the first study on the nonlinear analysis of heated CFSTs with high-strength concrete core containing steel fiber and waste tire rubber under axial compression loading. CFSTs had steel fibers with 0, 1, and 1.5% volume fractions and 0, 5, and 10% rubber particles as sand alternative material. They were subjected to 20, 250, 500, and 750℃ temperatures. Using flow rule and analytical analysis, a model is developed to predict the load bearing capacity of steel tube, and hoop strain-axial strain relationship, and axial stress-volumetric strain relationship of CFSTs. An elastic-plastic analysis method is applied to determine the axial and hoop stresses of the steel tube, considering elastic, yield, and strain hardening stages of steel in its stress-strain curve. The axial stress in the concrete core is determined as the difference between the total experimental axial stress and the axial stress of steel tube obtained from modeling. The results show that steel tube in CFSTs under 750℃ exhibits a higher load bearing contribution compared to those under 20, 250, and 500℃. It is also found that the ratio of load bearing capacity of steel tube at peak point to the load bearing capacity of CFST at peak load is noticeable such that this ratio is in the ranges of 0.21-0.33 and 0.31-0.38 for the CFST specimens with a steel tube thickness of 2 and 3.5 mm, respectively. In addition, after the steel tube yielding, the load bearing capacity of the tube decreases due to the reduction of its axial stiffness and the increase of hoop strain rate, which is in the range of about 20 to 40%.

• Computers and Concrete
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• v.29 no.6
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• pp.407-418
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• 2022

This paper numerically investigates the effect of changes in the mechanical properties (displacement, strain, and stress) of the ultra-high-performance concrete (UHPC) without rebar and the reinforced concrete (RC) using steel re-bars. This reinforced concrete is mostly used in the concrete bridge decks. A mixture of sand, gravel, cement, water, steel fiber, superplasticizer, and micro silica was used to fabricate UHPC specimens. The extended finite element method as used in the ABAQUS software is applied for considering the mechanical properties of UHPC, RC, and ordinary concrete specimens. To calibrate the ABAQUS, some experimental tests have been carried out in the laboratory to measure the direct tensile strength of UHPC by the compressive-to-tensile load converting (CTLC) device. This device contains a concrete specimen and is mounted on a universal tensile testing apparatus. In the experiments, three types of mixed concrete were used for UHPC specimens. The tensile strength of these specimens ranges from 9.24 to 11.4 MPa, which is relatively high compared with ordinary concrete specimens, which have a tensile strength ranging from 2 to 5 MPa. In the experimental tests, the UHPC specimen of size 150×60×190 mm with a central hole of 75 mm (in diameter)×60 mm (in thickness) was specially made in the laboratory, and its direct tensile strength was measured by the CTLC device. However, the numerical simulation results for the tensile strength and failure mechanism of the UHPC were very close to those measured experimentally. From comparing the numerical and experimental results obtained in this study, it has been concluded that UHPC can be effectively used for bridge decks.

• Computers and Concrete
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• v.31 no.3
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• pp.185-195
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• 2023

Antalya is located south part of minor Asia, one of the biggest cities in Türkiye. As a result of population growth and vast migration to Antalya, many parts of the city that were not suitable for construction due to its geological conditions have become urban areas, and most of these urban areas are full of poorly engineered buildings. Poor engineering has been combined with unplanned urbanization, that causes utter vulnerability to disasters in Antalya. When an earthquake-prone city, Antalya faces with an earthquake risk, fear arises in society. To overcome this problem, it has become necessary to investigate the building stock, expressed in hundreds of thousands, in a fast and reliable way and then perform an urban transformation to create the perception of structural safety. However, the excessive building stock, labor, and economic problems made the implementation stage challenging and revealed the necessity of finding alternative solutions in the field. The present study presents a novel approach for assessment and model based on a rapid visual inspection method to transform areas under earthquake risk in Türkiye. The approach aimed to rank the interventions for decision-making mechanisms by making comparisons in the scale hierarchy. In the present study, to investigate the proposed approach, over 26,000 buildings were examined in Antalya, which is the fifth largest city in Türkiye that has a population of over 2.5 Million. In the results of the study, the risk classification was defined in the framework of building, block, street, neighborhood, and district scales.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Computers and Concrete
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• v.31 no.5
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• pp.457-468
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• 2023

Buildings, bridges, and dams are examples of civil infrastructure that play an important role in public life. These structures are prone to structural variations over time as a result of external forces that might disrupt the operation of the structures, cause structural integrity issues, and raise safety concerns for the occupants. Therefore, monitoring the state of a structure, also known as structural health monitoring (SHM), is essential. Owing to the emergence of the fourth industrial revolution, next-generation sensors, such as wireless sensors, UAVs, and video cameras, have recently been utilized to improve the quality and efficiency of building forensics. This study presents a method that uses a target-based system to estimate the dynamic displacement and its corresponding dynamic properties of structures using UAV-based video. A laboratory experiment was performed to verify the tracking technique using a shaking table to excite an SDOF specimen and comparing the results between a laser distance sensor, accelerometer, and fixed camera. Then a field test was conducted to validate the proposed framework. One target marker is placed on the specimen, and another marker is attached to the ground, which serves as a stationary reference to account for the undesired UAV movement. The results from the UAV and stationary camera displayed a root mean square (RMS) error of 2.02% for the displacement, and after post-processing the displacement data using an OMA method, the identified natural frequency and damping ratio showed significant accuracy and similarities. The findings illustrate the capabilities and reliabilities of the methodology using UAV to evaluate the dynamic properties of structures.

• Computers and Concrete
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• v.31 no.6
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• pp.513-525
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• 2023

This research presents the experimental and theoretical evaluations on circular steel-fiber-reinforced-concrete (SFRC) columns reinforced by glass-fiber-reinforced-polymer (GFRP) rebar under the axial compressive loading. Test programs were designed to investigate and compare the effect of different parameters on the structural behavior of columns by performing tests. Theses variables included conventional concrete (CC), fiber concrete (FC), steel/GFRP longitudinal rebars, and transversal rebars configurations. A total of 16 specimens were constructed and categorized into four groups in terms of different rebar-concrete configurations, including GFRP-rebar-reinforced-CC columns (GRCC), GFRP-rebar-reinforced-FC columns (GRFC), steel-rebar-reinforced-CC columns (SRCC) and steel-rebar- reinforced-FC columns (SRFC). Experimental observations displayed that failure modes and cracking patterns of four groups of columns were similar, especially in pre-peak branches of load-deflection curves. Although the average ultimate axial load of columns with longitudinal GFRP rebars was obtained by 17.9% less than the average ultimate axial load of columns with longitudinal steel rebars, the average axial ductility index (DI) of them was gained by 10.2% higher than their counterpart columns. Adding steel fibers (SFs) into concrete led to the increases of 7.7% and 6.7% of the axial peak load and the DI of columns than their counterpart columns with CC. The volumetric ratio had greater efficiency on peak loads and DIs of columns than the type of transversal reinforcement. A simple analytical equation was proposed to predict the axial compressive capacity of columns by considering the axial involvement of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop rebar. There was a good correlation between test results and predictions of the proposed equation.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Computers and Concrete
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• v.32 no.4
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• pp.351-363
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• 2023

Reducing the self-weight of reinforced concrete structures problem is discussed in this paper by using two types of self-weight reduction, the first is by using lightweight coarse aggregate (crushed brick) and the second is by using styropor block. Experimental and Numerical studies are conducted on (LWAC) lightweight aggregate reinforced concrete slabs, having styropor blocks with various sizes of blocks and the ratio of shear span to the effective depth (a/d). The experimental part included testing eleven lightweight concrete one-way simply supported slabs, comprising three as reference slabs (solid slabs) and eight as styropor block slabs (SBS) with a total reduction in cross-sectional area of (43.3% and 49.7%) were considered. The holes were formed by placing styropor at the ineffective concrete zones in resisting the tensile stresses. The length, width, and thickness of specimen dimensions were 1.1 m, 0.6 m, and 0.12 m respectively, except one specimen had a depth of 85 mm (which has a cross-sectional area equal to styropor block slab with a weight reduction of 49.7%). Two shear spans to effective depth ratios (a/d) of (3.125) for load case (A) and (a/d) of (2) for load case (B), (two-line monotonic loads) are considered. The test results showed under loading cases A and B (using minimum shear reinforcement and the reduction in cross-sectional area of styropor block slab by 29.1%) caused an increase in strength capacity by 60.4% and 54.6 % compared to the lightweight reference slab. Also, the best percentage of reduction in cross-sectional area is found to be 49.7%. Numerically, the computer program named (ANSYS) was used to study the behavior of these reinforced concrete slabs by using the finite element method. The results show acceptable agreement with the experimental test results. The average difference between experimental and numerical results is found to be (11.06%) in ultimate strength and (5.33%) in ultimate deflection.