Although accurately estimating the early age compressive strength of concrete is essential for the timely removal of formwork and the advancement of construction processes, it is challenging to estimate it in cool, cold, hot, or unmanaged conditions. Various nondestructive testing methods, including recent IoT-based techniques, have been proposed to determine the compressive strength of concrete. This study evaluates the maturity method using the wireless thermocouple sensor in assessing the early age compressive strength of concrete slabs, particularly those not subjected to watering and protection in a cool environment below 20 °C. For this purpose, wire and wireless thermocouple sensors were installed in reinforced concrete (RC) slabs, whereas wire thermocouple sensors were installed in concrete cylinders. In addition, the compressive strengths of standard-cured cylinders, field-cured cylinders, and core samples extracted from the RC slab were measured. On day 7, the maturity index (M) values for the field-cured cylinders were 7% lower than those of the standard-cured cylinders, and the M values for the RC slabs with wire and wireless sensors were 6% lower. The compressive strengths of the field-cured cylinders and core samples extracted from the RC slabs were 19% and 14% lower than those of the standard-cured cylinders, respectively. Thus, while the difference in M values was 6–7%, the difference in compressive strength was significantly higher, at 14–19%. In a cool environment without watering or protection, the difference in strength can be even greater. Consequently, a commercial IoT-based thermocouple sensor can replace conventional wire sensors and adopt to estimate early age compressive strength of concrete in unmanaged curing condition.

This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously.

Predicting the damage to a concrete panel under impact loading is difficult due to the complexity of the impact mechanism of concrete. Based on the experimental results obtained by various researchers, the energies involved in the impact mechanism are classified into seven categories: Kinetic energy, deformed energy of a projectile, elastic penetration resistance energy of the panel, overall deformed energy of the panel, spalling-resistant energy, tunneling-resistant energy, and scabbing-resistant energy. Using these impact mechanisms and the energy conservation law, a new energy-based penetration depth formula is proposed to predict the penetration depth. This is validated using 402 impact test results, which include those with high-strength concrete, ultra-high-performance concrete (UHPC), or steel fiber-reinforced concrete, those under very high-velocity impact, and those with a very low ratio of target panel thickness to projectile diameter. It is found that the new impact formula predicts the penetration depth quite well.

The mechanical properties of lightweight aggregate concrete developed with the use of bottom ash aggregate (LWAC-BA) as a partial or full replacement of lightweight aggregate differ from those of general lightweight concrete made using natural fine and/or coarse aggregates. The mechanical properties of LWAC-BA are difficult to predict using the existing equations proposed by codes or researchers. Therefore, in this study, empirical equations using nonlinear regression analysis are proposed to predict the mechanical properties of lightweight concrete mixed with bottom ash aggregate, based on the collected measured values from other studies (Yang "Development of replacement technology for ready mixed concrete with bottom ash aggregates", 2020; Kim et al. Appl Sci, 10: e8016, 2020; Constr Build Mater 273: e121998, 2021). The collected data include density, compressive strength, elastic modulus, modulus of rupture, splitting tensile strength, and stress–strain relation of LWAC-BA featuring varying amounts of bottom ash fine aggregate and/or coarse aggregate. The proposed empirical equations for each mechanical characteristic are developed considering the replacement volume of bottom ash fine/coarse aggregates. The mean values of the ratios of the measured to predicted values obtained using the proposed equation range from 1.00 to 1.05, with a standard deviation ranging from 0.002 to 0.013, indicating a reasonably positive agreement.