To date, a method of attaching a FRP (fiber-reinforced polymer) to concrete members with epoxy has been widely applied to increase the strength of the member. However, there are cases in which the adhesion of the epoxy deteriorates over time and the reinforcing effect of the FRP is gradually lost. Therefore, monitoring whether or not the reinforcing effect is properly maintained is needed in order to prevent a decrease in the structural performance of the member improved by FRP reinforcement. In this regard, this study examines FRP with OF (optical fiber) sensors to monitor the reinforcing effect of FRP in concrete structural members. In particular, this paper seeks to determine an appropriate adhesion length when FBG (fiber Bragg grating) based OF sensors are externally bonded to FRP strips with epoxy resin. To this end, a tensile test was carried out to evaluate the sensing performance according to the adhesion length. In addition, an analytical approach was performed and the result were compared with test result. The results of the experimental and analytical studies showed that the strain generated in the FRP is sufficiently transferred to the OF if the total adhesion length of it is 40 mm or more in consideration of the error in the epoxy thickness.

Two types of multi-walled carbon nanotubes (MWCNTs), powder and liquid, were added to cementitious composites to build self-sensing concrete. To properly evaluate and quantify the effect of MWCNTs on electrical resistance, various tests, including isothermal conduction calorimetry, were carried out. One of primary issues of self-monitoring concrete is dispersion, so ultrasonication was used to properly mix the CNTs in the dispersion solution, and silica fume was employed to make the specimens. Scanning electron microscopy (SEM), Raman spectroscopy, and porosity analyses were performed to investigate the physical properties of the composites and to confirm uniform dispersion. The distance of the electrical resistance was also measured, and the dosages and types of MWCNTs were analyzed.

The effectiveness of seismic retrofitting using three different fibers—carbon fiber (CF), glass fiber (GF), polyethylene terephthalate (PET) fiber—and a fiber combination of aramid fiber (AF) and PET fiber (called hybrid fiber reinforced polymer (HF)) wrapped on reinforced concrete (RC) circular columns was experimentally evaluated. A total of 11 RC circular columns were tested: three control columns and eight retrofitted columns in three different test groups. The purpose of fiber wrapping was flexural strength improvement as well as enhancement of rotational capacity in the plastic hinge region. Mechanical properties of CF, GF, AF, and PET were first defined; that is, CF, GF, and AF exhibited linear stress–strain behavior with limited ultimate strain capacity typically less than 3%, while ductile PET exhibited as much as 15% strain and non-linear stress–strain behavior with a very low elastic modulus. In the RC column tests, all three different fibers and the AF + PET fiber combination were effective in enhancing the strength and ductility but resulted in different structural behaviors and failure modes depending on the fiber type and the fiber amount used. The column sections were then analytically studied by section analysis using the behavior of confined concrete, the non-linear relationship of fiber-reinforced polymer (FRP), and the actual material properties of reinforcement. The analytical and experimental results revealed that ductile PET is beneficial, as it demonstrates more ductile behavior with a degree of strength enhancement similar to that of CF and GF.

Earthquake Early Warning (EEW) is an alert system, based on seismic wave propagation theory, to reduce human casualties. EEW systems mainly utilize technologies through both network-based and on-site methods. The network-based method estimates the hypocenter and magnitude of an earthquake using data from multiple seismic stations, while the on-site method predicts the intensity measures from a single seismic station. Therefore, the on-site method reduces the lead time compared to the network-based method but is less accurate. To increase the accuracy of on-site EEW, our system was designed with a hybrid method, which included machine learning algorithms. At this time, machine learning was used to increase the accuracy of the initial P-wave identification rate. Additionally, a new approach using a nearby seismic station, called the 1+ α method, was proposed to reduce false alarms. In this study, an on-site EEW trial operation was performed to evaluate its performance. The warning cases for small and large events were reviewed and the possibility of stable alert decisions was confirmed.