In this study, sprayable strain-hardening fiber-reinforced cementitious composites (FRCC) were applied to strengthen the concrete slabs in a concrete-face rockfill dam (CFRD) for the first time. Experimental, numerical, and analytical investigations were carried out to understand the flexural properties of FRCC-layered concrete slabs. It was found that the FRCC layer improved the flexural performance of concrete slabs significantly. The cracking and ultimate loads of a concrete slab with an 80 mm FRCC layer were 132% and 69% higher than those of the unstrengthened concrete slab, respectively. At the maximum crack width of 0.2 mm, the deflection of the 80-mm FRCC strengthened concrete slab was 144% higher than that of the unstrengthened concrete slab. In addition, a FE model and a simplified analytical method were developed for the design and analysis of FRCC-layered concrete slabs. Finally, the test result of FRCC leaching solution indicated that the quality of the water surrounding FRCC satisfied the standard for drinking water. The findings of this study indicate that the sprayable strain-hardening FRCC has a good potential for strengthening hydraulic structures such as CFRDs.
In sugar industries, the growing amount of sugarcane bagasse ash (SBA), a byproduct released after burning bagasse for producing electricity, is currently causing environmental pollution. The residual ash displays a pozzolanic potential; and hence, it has potential as a cement addictive. This study focuses on enhancing suitability of SBA through incorporating ground blast furnace slag (BFS) in manufacturing self-compacting concretes (SCCs). For this purpose, SBA was processed by burning at 700 °C for 1 h, before being ground to the cement fineness of 4010 cm2/g. SCC mixtures were prepared by changing the proportions of SBA and BFS (i.e., 10%, 20%, and 30%) in blended systems; and their performance was investigated. Test results showed that the presence of amorphous silica was detected for the processed SBA, revealing that the strength activity index was above 80%. The compressive strength of SCC containing SBA (without BFS) could reach 98%−127% of that of the control; combination of SBA and 30% BFS gets a similar strength to the control after 28 d. Regarding durability, the 10%SBA + 30%BFS mix exhibited the lowest risk of corrosion. Moreover, the joint use of SBA and BFS enhanced significantly the SCC’s sulfate resistance. Finally, a hyperbolic formula for interpolating the compressive strength of the SBA-based SCC was proposed and validated with error range estimated within ±10%.
The corrosion degradation behavior of concrete materials plays a crucial role in the change of its mechanical properties under multi-ion interaction in the marine environment. In this study, the variation in the macro-physical and mechanical properties of concrete with corrosion time is investigated, and the source of micro-corrosion products under different salt solutions in seawater are analyzed. Regardless of the continuous hydration effect of concrete, the damage effects of various corrosive ions (Cl–,
A new insight into the interfacial transition zone (ITZ) in cement mortar specimens (CMSs) that is influenced by cement fineness is reported. The importance of cement fineness in ITZ characterizations such as morphology and thickness is elucidated by backscattered electron images and by consequences to the compressive (Fc) and flexural strength (Ff), and porosity at various water/cement ratios. The findings indicate that by increasing the cement fineness the calcium silicate hydrate formation in the ITZ is favored and that this can refine the pore structures and create a denser and more homogeneous microstructure. By increasing cement fineness by about 25% of, the ITZ thickness of CMSs was reduced by about 30% and Fc was increased by 7%–52% and Ff by 19%–40%. These findings illustrate that the influence of ITZ features on the mechanical strength of CMSs is mostly related to the cement fineness and ITZ microstructure.
With rapid urbanization in China, a large amount of excavated soil and construction sludge is being generated from geotechnical and underground engineering. For sustainable management of these construction wastes, it is essential to quantify their production first. The present study has attempted to classify the excavated soil and construction sludge according to their composition and geotechnical properties (particle size, water content, plasticity index). Based on these classifications, a new approach was proposed to quantify the production. The said approach was based on multi-source information, such as the urban topographic map, geological survey reports, urban master plan, and remote sensing images. A case study in Wenzhou city of China was also pursued to illustrate the validity of the newly developed approach. The research showed that in 2021–2025, the total excavated soils and construction sludge production in Wenzhou would reach 107.5 × 106 and 81.7 × 106 m3, respectively. Furthermore, the excavated soil was classified into the miscellaneous fill, crust clay, muddy clay and mud with silty sand. Likewise, the construction sludge was classified as liquid sludge and paste-like sludge. The classification and quantification can serve as guidance for disposal and recycling, thereby leading to high-level management of waste disposal.
In recent years, great attention has focused on the development of automated procedures for infrastructures control. Many efforts have aimed at greater speed and reliability compared to traditional methods of assessing structural conditions. The paper proposes a multi-level strategy, designed and implemented on the basis of periodic structural monitoring oriented to a cost- and time-efficient tunnel control plan. Such strategy leverages the high capacity of convolutional neural networks to identify and classify potential critical situations. In a supervised learning framework, Ground Penetrating Radar (GPR) profiles and the revealed structural phenomena have been used as input and output to train and test such networks. Image-based analysis and integrative investigations involving video-endoscopy, core drilling, jacking and pull-out testing have been exploited to define the structural conditions linked to GPR profiles and to create the database. The degree of detail and accuracy achieved in identifying a structural condition is high. As a result, this strategy appears of value to infrastructure managers who need to reduce the amount and invasiveness of testing, and thus also to reduce the time and costs associated with inspections made by highly specialized technicians.
The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy ( R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.
Damping is known to have a considerable influence on the dynamic behavior of bridges. The fixed damping ratios recommended in design codes do not necessarily represent the complicated damping characteristics of bridge structures. This study investigated the application of stress-dependent damping associated with vehicle-bridge coupling vibration and based on that investigation proposed the stress-dependent damping ratio. The results of the investigation show that the stress-dependent damping ratio is significantly different from the constant damping ratio (5%) defined in the standard specification. When vehicles travel at speeds of 30, 60, and 90, the damping ratios of the bridge model are 3.656%, 3.658%, and 3.671%, respectively. The peak accelerations using the regular damping ratio are 18.9%, 21.3%, and 14.5% of the stress-dependent damping ratio, respectively. When the vehicle load on the bridge is doubled, the peak acceleration of the mid-span node increases by 5.4 times, and the stress-related damping ratio increases by 2.1%. A corrugated steel-web bridge is being used as a case study, and the vibration response of the bridge is compared with the measured results. The acceleration response of the bridge which was calculated using the stress-dependent damping ratio is significantly closer to the measured acceleration response than that using the regular damping ratio.
In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R2 than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.
