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Buried pipelines under various soil embankment heights are cost-effective alternatives to transporting liquid products. This paper aims to assist pipeline architects and professionals in selecting the most cost-effective buried reinforced concrete pipelines under deep embankment soil with minor structural reinforcement while meeting shear stress requirements, safety and reliability constraints.

It is unfeasible to experimentally assess pipeline efficiency with high soil fill depth. Thus, to fill this gap, this research uses a dependable finite element analysis (FEA) to conduct a parametric study and carry out such an issue. This research considered reinforced concrete pipes with diameters of 25, 50, 75, 100, 125 and 150 cm at depths of 5, 10, 15 and 20 m.

According to this research, the proposed best pipeline diameter-to-thickness (D/T) proportions for soil embankment heights 5, 10, 15 and 20 m are 8.75, 4.8, 3.5 and 3.1, correspondingly. The cost-effective reinforced concrete (RC) pipeline thickness dramatically rises if the soil embankment reaches 20 m, indicating that the soil embankment depth highly influences it. Most of the analyzed reinforced concrete pipelines had a maximum deflection value of less than 1 cm, telling that the FEA accurately identified the pipeline width, needed flexural steel reinforcement, and concrete crack width while avoiding significant distortion.

The cost-effective thickness for the analyzed structured concrete pipes was calculated by considering the lowest required value of steel reinforcement. An algorithm was developed based on the parametric scientific findings to predict the ideal pipeline D/T ratio. A construction case study was also shown to assist architects and professionals in determining the best reinforced concrete pipeline geometry for a specific soil embankment height.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The combination of an Engineered Cementitious Composite (ECC) layer and steel plate to reinforce RC beams (ESRB) is a new strengthening method. The ESRB was proposed based on the steel plate at the bottom of RC beams, aiming to solve the problem of over-reinforced RC beams and improve the bearing capacity of RC beams without affecting their ductility.

In this paper, the finite element model of ESRB was established by ABAQUS. The results were compared with the experimental results of ESRB in previous studies and the reliability of the finite element model was verified. On this basis, parameters such as the width of the steel plate, thickness of the ECC layer, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar were analyzed by the verified finite element model. Based on the load–deflection curve of ESRB, ESRB was discussed in terms of ultimate bearing capacity and ductility.

The results demonstrate that when the width of the steel plate increases, the ultimate load of ESRB increases to 133.22 kN by 11.58% as well as the ductility index increases to 2.39. With the increase of the damage degree of the original beam, the ultimate load of ESRB decreases by 23.7%–91.09 kN and the ductility index decreases to 1.90. With the enhancement of the cross-sectional area of longitudinal tensile rebar, the ultimate bearing capacity of ESRB increases to 126.75 kN by 6.2% and the ductility index elevates to 2.30. Finally, a calculation model for predicting the flexural capacity of ESRB is proposed. The calculated results of the model are in line with the experimental results.

Based on the comparative analysis of the test results and numerical simulation results of 11 test beams, this investigation verified the accuracy and reliability of the finite element simulation from the aspects of load–deflection curve, characteristic load and failure mode. Furthermore, based on load–deflection curve, the effects of steel plate width, ECC layer thickness, damage degree of the original beam and cross-sectional area of longitudinal tensile rebar on the ultimate bearing capacity and ductility of ESRB were discussed. Finally, a simplified method was put forward to further verify the effectiveness of ESRB through analytical calculation.

Recently, the repairing of reinforced concrete (RC) structures attracted great research attentions, but the research interests were mainly concentrated on common repairing types. To this end, in this paper, a repairing of pre-loaded RC beams strengthened by aramid reinforcement polymers (AFRP) is presented. Furthermore, the purpose of this paper is to study the behavior of pre-loaded RC Deep beams under sustained load. The AFRP has many advantages such as controlling stresses distribution around the openings, controlling failure modes, and enhancing the structural capacity of pre-cracked RC beams.

Four specimens were experimentally tested: one specimen without strengthening, which is considered as control specimen, one strengthened specimen using AFRP without pre-cracking and two specimens subjected to pre-cracking load before prior to AFRP application. Furthermore, after validation of experimental data by using ANSYS software, a parametric study was conducted to investigate the effect of pre-damage level on shear capacity of RC beams. For pre-cracked beams, loading was first applied until the cracking stage, followed by specimen repairing with epoxy injection, and then the specimens were loaded again until failure point.

The result showed that pre-damage level and AFRP strengthening have great influence on the ultimate strength and failure mode. In addition, the results obtained from experimental tests were compared with those from numerical validation with ANSYS and showed good agreement.

Based on ACI guidelines, an analytical equation for calculating the shear strength of strengthened RC beams with openings subjected to pre-damage was then proposed, and the calculated results were compared with those from the tests, with differences not exceeding 10%.

The purpose of this paper is to propose a method that can calculate the transportation infrastructure network service capacity enhancement given by planned transportation infrastructure projects construction. Managers can sequence projects more rationally to maximize the construction effectiveness of infrastructure investments.

This paper designed a computational network simulation software to generate topological networks based on established rules. Based on the topological networks, the software simulated the movement path of users and calculated the average travel time. This software allows the adjustment of parameters to suit different research objectives. The average travel time is used as an evaluation index to determine the most appropriate construction sequence.

In this paper, the transportation infrastructure network of Sichuan Province in China was used to demonstrate this software. The average travel time of the existing transportation network in Sichuan Province was calculated as 211 min using this software. The high-speed railways from Leshan to Xichang and from Xichang to Yibin had the greatest influence on shortening the average travel time. This paper also measured the changes in the average travel time under two strategies: shortening the maximum and minimum priorities. All the transportation network optimisation plans for Sichuan Province will be somewhere between these two strategies.

The contribution of this research are three aspects: First, a complex network analysis method that can take into account the differences of node elements is proposed. Second, it provides an effective tool for decision makers to plan transportation infrastructure construction. Third, the construction sequence of transportation infrastructure development plan can effect the infrastructure investment effectiveness.

Regular monitoring of bacteria, especially Escherichia coli, in wastewater is crucial to ensure the maintenance of public health. Amperometric detection proves to be a fast, sensitive and economically viable solution for E. coli enumeration. This paper reported a prototype amperometric sensor based on PANI-ZnO-NiO nanocomposite thin films prepared by sol–gel method and irradiated with gamma ray. The purpose of this study is to investigate the sensor performance of PANI-ZnO-NiO nanocomposite thin films to detect E. coli in water.

The films were varied with different compositions of ZnO and NiO by using the formula PANI-(ZnO)_1-x-(NiO)_x, with x = 0.2, 0.4, 0.6 and 0.8. PANI-ZnO-NiO nanocomposite thin films were characterized by using X-ray diffraction (XRD) and atomic force microscopy (AFM) to study the crystallinity and surface morphology of the films. The sensor performance was conducted using the current–voltage (I-V) measurement by testing the films in clean water and E. coli solution.

XRD diffractograms show the peaks of ZnO (1 0 0) and NiO (1 0 2). AFM analysis shows the surface roughness, and the grain size of PANI-ZnO-NiO thin films decreases when the concentration ratios of NiO increased. I-V curves show the difference in current flow, where the current in E. coli solution is higher than the clean water.

PANI-(ZnO)_1-x-(NiO)_x nanocomposite thin film with the highest concentration of ZnO performed the highest sensitivity among the other concentrations, which can be used to indicate the presence of E. coli bacteria in water.

Bacterial cellulose (BC) is an ideal alternative filtering material. However, current functionalization approaches for BC have not been fully discovered industrially as well as academically applying textile processing. This study aims to create a sustainable fabric-like membrane made of BC/activated carbon (AC) for applications in filtration using textile padding method, to protect people from respiratory pandemics.

Fabric-like BC is first mechanically dehydrated then AC is loaded via a textile padding step. The finishing efficacy, properties of fabric-like BC/AC and NaOH pretreatment are analyzed and characterized by scanning electron microscope (SEM), field emission scanning electron microscope (FE SEM), X-ray diffraction (XRD), CIELab color space, color strength (K/S), nitrogen adsorption-desorption isotherm including Brunauer–Emmett–Teller (BET) specific surface area and Barrett–Joyner–Halenda (BJH) pore size and volume.

This research results in a fabric-like BC/AC with pore diameters of 3.407 ± 0.310 nm, specific surface area of 115.28 m²/g and an efficient scalable padding process, which uses 8 times less amount of chemical and nearly 30 times shorter treating duration than conventional methods.

Our globe is now consuming an alarming amount of non-degradable disposable masks resulting in massive trash buildup as a future environmental problem. Besides, current disposable masks requiring a significant upfront technological investment have posed challenges in human protection from respiratory diseases, especially for countries with limited conditions. By combining a sustainable material (BC) with popular padding method of textile industry, the fabric-like BC/AC will offer sustainable and practical values for both humankind and nature.

This research has offered an effective padding process to functionalize BC, and a unique fabric-like BC/AC membrane for filtration applications.

Renewable energy alternatives and nanoscale materials have gained huge attention in recent years due to the problems associated with fossil fuels. The recyclable battery is one of the recent developments to address the energy requirement issues. In this work, the development of nanoscale materials is focused on using green synthesis methods to address the energy requirements of hybrid electric vehicles.

The current research focuses on developing metal oxide nanoscale materials (NANO-SMs). The Zno-Aloe vera NANO-SM is prepared using the green synthesis method. The developed nanoscale materials are characterized using analysis methods like FESEM, TEM, XRD and FTIR.

The average size of ZnO-Aloe vera mono-crystalline was recorded as 60–70 nm/Hexagonal shape. The nanoscale materials are used for the detection of LPG gases. The sensitivity observed was 48%. The response time and recovery time were recorded as 8–10 s and 230–250 s, respectively. The average size of SnO₂-green papaya leaves poly-crystalline was recorded as 10–20 nm/powder form.

Nanoscale materials are developed using green synthesis methods for hybrid vehicle applications. The nanoscale materials are used for the detection of harmful gases in hybrid vehicles.

This paper aims to investigate the corrosion resistance of two types of coatings – one is ceria sol coating and the other is ceria sol coating modified by ZnO nanoparticles on 7075 aluminum alloy in 3.5% NaCl solution.

Aluminum alloys were dipped into ceria sol and ceria sol modified by ZnO nanoparticles separately and removed after 10 min from the solutions and dried at 110°C for 30 min and heated at 500 °C for 30 min to form the coatings. The coatings have been characterized by using field emission scanning electron microscopy (FE-SEM), electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS). The EIS tests were performed in a corrosive solution of 3.5% NaCl.

The results showed that the coating of ceria sol modified by ZnO nanoparticles has higher corrosion resistance than the ceria sol coating and the bare sample. Also, the best efficiency is related to aluminum sample immersion after 1 h in NaCl corrosive solution for coating modified by ZnO nanoparticles.

In this research, the modification of ceria sol coating by ZnO nanoparticles had an effect on improving the corrosion behavior of aluminum alloy. It is also understood that modification of coatings is an effective parameter on corrosion resistance.

This study aims to investigate profoundly the protection of oil painting from deterioration using molybdenum trisulphide quantum dots (MoS3 QDs) against microbe, dirt accumulation and ultraviolet (UV) degradation.

The protection of painting against different deterioration factors necessitates the sustainable methods and advanced techniques. Scanning electron microscopy and transmission electron microscopy have been used to investigate the morphological structure of the painting and MoS3 QDs, respectively, and optical microscopy was used to examine antibacterial activity of MoS3 QDs towards different types of bacteria. To investigate the protection of painting against deterioration, the Fourier transform IR spectroscopy (FTIR) was used to investigate the paintings left in open air for a year. Chemical composition and crystal structure of MoS3 QDs have been studied using X-ray diffraction and X-ray photoelectron spectroscopy analysis, respectively.

The addition of MoS3 nanoparticles into painted coatings enhances the durability of linseed oil-based paintings toward UV ageing regarding the change in colour which confirmed by FTIR analysis. The protection of oil painting opposed to various deterioration factors was developed by involving of MoS3 QDs in the coating of the painting. Antibacterial effect of MoS3 QDs was tested against different types of bacteria such as Pseudomonas aeruginosa confirming that the MoS3 QDs involved in the coatings of oil paintings produces a high protection layer for the paintings against several microbial attacks. In addition, coatings containing MoS3 QDs reduce the accumulation of dirt on oil paintings when subjected to open air for a year.

The novel MoS3 QDs was used to form a protective and transparent coating layer for the oil painting to overcome the deterioration, displays the promising protection and can be applied for different oil paintings.