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To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

Structural health monitoring (SHM) and safety assessment are very important areas for evaluating the behavior of structures. Various wired and wireless sensors can measure the physical responses of structures, such as displacement or strain. One recently developed wireless technique is a light imaging detection and ranging (LiDAR) system that can remotely acquire three-dimensional (3D) high-precision coordinate information using 3D laser scanning. LiDAR systems have been previously used in geographic information systems (GIS) to collect information on geography and terrain. Recently, however, LiDAR is used in the SHM field to analyze structural behavior, as it can remotely detect the surface and deformation shape of structures without the need for attached sensors. This study demonstrates a strain evaluation method using a LiDAR system in order to analyze the behavior of steel structures. To evaluate the strains of structures from the initial and deformed shape, a combination of distributed 3D point cloud data and finite element methods (FEM) was used. The distributed 3D point cloud data were reconstructed into a 3D mesh model, and strains were calculated using the FEM. By using the proposed method, the strain could be calculated at any point on a structure for SHM and safety assessment during construction., Competing Interests: The authors declare no conflict of interest.

To investigate the effects of sulfate on CO2 attack on wellbore cement (i.e., chemical and mechanical alterations) during geologic CO2 sequestration (GCS), we reacted cement samples in brine with 0.05 M sulfate and 0.4 M NaCl at 95 °C under 100 bar of either N2 or supercritical CO2. The results were compared to those obtained from systems without additional sulfate at the same temperature, pressure, salinity, and initial brine pHs. After 10 reaction days, chemical analyses using scanning electron microscopy with a backscattered electron detector (SEM-BSE) and inductively coupled plasma optical emission spectrometry (ICP-OES) showed that the CO2 attack in the presence of additional sulfate was much less severe than that in the system without additional sulfate. The results from three-point bending tests also indicated that sulfate significantly mitigated the deterioration of the cement's strength and elastic modulus. In all our systems, typical sulfate attacks on cement via formation of ettringite were not observed. The protective effects of sulfate on cement against CO2 attack resulted from sulfate adsorption, coating of CaSO4 on the CaCO3 grains in the carbonated layer, or both, which inhibited dissolution of CaCO3. Findings from this study provide new, important information for understanding the integrity of wellbores at GCS sites and thus promote safer GCS operations.

To provide information on wellbore cement integrity in the application of geologic CO2 sequestration (GCS), chemical and mechanical alterations were analyzed for cement paste samples reacted for 10 days under GCS conditions. The reactions were at 95 °C and had 100 bar of either N2 (control condition) or CO2 contacting the reaction brine solution with an ionic strength of 0.5 M adjusted by NaCl. Chemical analyses showed that the 3.0 cm × 1.1 cm × 0.3 cm samples were significantly attacked by aqueous CO2 and developed layer structures with a total attacked depth of 1220 μm. Microscale mechanical property analyses showed that the hardness and indentation modulus of the carbonated layer were 2-3 times greater than for the intact cement, but those in the portlandite-dissolved region decreased by ∼50%. The strength and elastic modulus of the bulk cement samples were reduced by 93% and 84%, respectively. The properties of the microscale regions, layer structure, microcracks, and swelling of the outer layers combined to affect the overall mechanical properties. These findings improve understanding of wellbore integrity from both chemical and mechanical viewpoints and can be utilized to improve the safety and efficiency of CO2 storage.