The structural performance of concrete structures subjected to fire is greatly influenced by the heating temperature. Therefore, an accurate estimation of the heating temperature is of vital importance for deriving a reasonable diagnosis and assessment of fire-damaged concrete structures. In current practice, various heating temperature estimation methods are used, however, each of these estimation methods has limitations in accuracy and faces disadvantages that depend on evaluators’ empirical judgments in the process of deriving diagnostic results from measured data. Therefore, in this study, a concrete heating test and a non-destructive test were carried out to estimate the heating temperatures of fire-damaged concrete, and a heating temperature estimation method using an adaptive neuro-fuzzy inference system (ANFIS) algorithm was proposed based on the results. A total of 73 datasets were randomly extracted from a total of 87 concrete heating test results and we used them in the data training process of the ANFIS algorithm; the remaining 14 datasets were used for verification. The proposed ANFIS algorithm model provided an accurate estimation of heating temperature.

In this study, shear tests were conducted to investigate the effects of longitudinal reinforcement corrosion on the shear capacity of reinforced concrete (RC) members with transverse reinforcement. To this end, a total of eight test specimens were fabricated, and the corrosion rates and anchorage details of rebars were set as test variables. In addition, an accelerated corrosion technique was used to introduce corrosion into the longitudinal reinforcement without corroding shear reinforcement. The test results indicated that the capacities of the specimens in which tension reinforcement was not properly anchored at the ends of the members decreased rapidly at high corrosion rates, whereas the capacities of the specimens in which tension reinforcement was properly anchored by hooks were similar to or higher than those of the non-corroded specimens, despite bond loss caused by corrosion.

In this study, full-scale fire tests and finite element (FE) analyses are conducted to investigate the fire resistance performance of hollow-core slabs (HCSs) manufactured using the extrusion method. The deflection of the HCS specimens and the temperature distribution in the section according to the fire exposure time are measured and analyzed comprehensively, and the test results are compared with the FE analysis results. In addition, parametric analyses are conducted on 21 cases with the HCS depth, span length, hollow ratio in a section, cover thickness of concrete, and load ratio (i.e., the ratio of the external load to the ultimate load) as variables, based on which the fire resistance performance of the HCS according to each variable is investigated. The analysis results show that the load ratio is a key factor governing the fire resistance behavior of HCSs, whereas the effects of the cover thickness of concrete and the hollow ratio in a section are relatively slight within the range of variables examined in this study.

When fire occurs in a large multiplex building, the direction of smoke and flames is often similar to that of the evacuation of building occupants. This causes evacuation bottlenecks in a specific compartment, especially when the occupant density is very high, which unfortunately often leads to many fatalities and injuries. Thus, the development of an egress model that can ensure the safe evacuation of occupants is required to minimize the number of casualties. In this study, the correlations between fire temperature with visibility and toxic gas concentration were investigated through a fire simulation on a multiplex building, from which databases for training of artificial neural networks (ANN) were created. Based on this, an ANN model that can predict the available safe egress time was developed, and it estimated the available safe egress time (ASET) very accurately. In addition, an egress model that can guide rapid and safe evacuation routes for occupants was proposed, and the rationality of the proposed model was verified in detail through an application example. The proposed model provided the optimal evacuation route with the longest margin of safety in consideration of both ASET and the movement time of occupants under fire.

Nursing hospitals have a high probability of casualties during a fire disaster because they have many patients with impaired mobility. In this study, fire and egress simulations were conducted to evaluate the egress safety of a typical nursing hospital. The available safe egress time (ASET) of the prototype nursing hospital was calculated using Fire Dynamics Simulator, and the required safe egress time (RSET) was estimated by Pathfinder, reflecting characteristics of the occupants. The egress safety of the nursing hospital was then evaluated by comparing the ASET and RSET, considering the number of egress guides and delay time. According to the simulation results, the RSET increased as the egress delay time increased and the number of egress guides decreased. In addition, it is estimated that at least 20 workers (egress guides) should be on duty in the prototype nursing hospital, even during shiftwork and night duty. Based on the simulation results, egress safety criteria have been proposed in terms of normalized numbers of egress guides and egress delay time. The proposed criteria can be very easily applied to evaluate the egress safety of a typical nursing hospital in operation.