This paper aims to develop a novel prediction tool based on the machine learning framework to evaluate the compressive strength and effective porosity of pervious concrete material from its compositions. To address this difficult task, 14 data sources were collected from the literature to build a dataset of 164 samples. The dataset included seven mixture design features (e.g., aggregate-to-cement ratio, water-to-cement ratio, minimum coarse aggregate size, the presence of sand or silica fume, effective porosity, and the compressive strength). This dataset was trained and tested by the most relevant machine learning methods: the extreme gradient boosting method (XGB), the random forest regression method, and the support vector machine method. The Particle Swarm Optimization method was applied to tune the models’ hyperparameters. It was observed that the extreme gradient boosting method significantly outperformed the accuracy of the other methods. Relatively high R-squared values of 0.92 and 0.88 were obtained for the compressive strength and effective porosity predictions. Furthermore, to account for the role of compaction, the original database was refined to obtain a 36 samples subset that considered compaction energy. Based on our assessment of this subset, results yielded superior R-squared values up to 0.99 for compressive strength, and 0.97 for effective porosity, revealing the effectiveness and accuracy of this research.

The purpose of this study is to identify the differences in porosity and permeability coefficients when the mixing ratio of aggregates is different and to present the mixing ratio satisfying the strength requirement of compressive specified in a specification of Korea. Three mix ratios were suggested by considering various aggregate sizes and three cylinders were made for each ratio. The porosities of those cylinders were evaluated through the compression and water permeability test, measuring the weight of specimens in underwater and analysis of the pictured Computed Tomography (CT) image. Experiments have shown that it is best to mix 50% for 5–10 mm aggregates, 45% for 2–5 mm aggregates, and 5% for sand in terms of strength and permeability. In addition, as the proportion of fine aggregates increased, the porosity and permeability decreased. Moreover, the effectiveness of maintenance method was also examined in this study.

Conventional methods for seismic retrofitting of concrete columns include reinforcement with steel plates or steel frame braces, as well as cross-sectional increments and in-filled walls. However, these methods have some disadvantages, such as the increase in mass and the need for precise construction. Fiber-reinforced polymer (FRP) sheets for seismic strengthening of concrete columns using new light-weight composite materials, such as carbon fiber or glass fiber, have been developed, have excellent durability and performance, and are being widely applied to overcome the shortcomings of conventional seismic strengthening methods. Nonetheless, the FRP-sheet reinforcement method also has some drawbacks, such as the need for prior surface treatment, problems at joints, and relatively expensive material costs. In the current research, the structural and material properties associated with a new method for seismic strengthening of concrete columns using FRP were investigated. The new technique is a sprayed FRP system, achieved by mixing chopped glass and carbon fibers with epoxy and vinyl ester resin in the open air and randomly spraying the resulting mixture onto the uneven surface of the concrete columns. This paper reports on the seismic resistance of reinforced concrete columns controlled by shear strengthening using the sprayed FRP system. Five shear column specimens were designed, and then strengthened with sprayed FRP by using different combinations of short carbon or glass fibers and epoxy or vinyl ester resins. There was also a non-strengthened control specimen. Cyclic loading tests were carried out, and the ultimate load carrying capacity and deformation were investigated, as well as hysteresis in the lateral load-drift relationship. The results showed that shear strengths and deformation capacities of shear columns strengthened using sprayed FRP improved markedly, compared with those of the control column. The spraying FRP technique developed in this study can be practically and effectively used for the seismic strengthening of existing concrete columns.

This paper presents an experimental work to study the flexural strength of reinforced concrete (RC) beams strengthened by partially de-bonded near surface-mounted (NSM) fiber reinforced polymer (FRP) strip with various de-bonded length. Especially, considering high anchorage capacity at end of a FRP strip, the effect of de-bonded region at a central part was investigated. In order to check the improvement of strength or deformation capacity when the bonded surface area only increased without changing the FRP area, single and triple lines of FRP were planned. In addition, the flexural strength of the RC member strengthened by a partially de-bonded NSM FRP strip was evaluated by using the existing researchers’ strength equation to predict the flexural strength after retrofit. From the study, it was found that where de-bonded region exists in the central part of a flexural member, the deformation capacity of the member is expected to be improved, because FRP strain is not to be concentrated on the center but to be extended uniformly in the de-bonded region. Where NSM FRP strips are distributed in triple lines, a relatively high strength can be exerted due to the increase of bond strength in the anchorage.