Predictive worker safety assessment through on-site correspondence using multi-layer fuzzy logic in outdoor construction environments.

Construction sites remain highly perilous work environments globally, exposing employees to numerous hazards that can result in severe injuries or fatalities. To resolve this several solutions based on quantitative approaches have been developed. However the wide adoption of preexisting solutions is hindered by lack of accuracy. To this aim the development of an efficient fuzzy inference system has become a de-facto necessity. In this paper, we propose an edge inference framework based on multi-layered fuzzy logic for safety of construction workers. The proposed system employs an edge computing-based framework where IoT devices collect, store, and manage data to offer safety services. Multi-layer fuzzy logic is applied to infer the worker safety index based on rules that consist of construction environment factors. The multi-layer fuzzy logic is fed with weather, building and worker data collected from IoT nodes as inputs. The safety risk assessment process involves analyzing various factors. Weather information, such as temperature, humidity, and rainfall data, is considered to assess the risk to safety. The condition of the building is evaluated by analyzing load, strain, and inclination data. Additionally, the safety risk to workers is analyzed by taking into account their heart rate and location information. The initial layer's outputs are utilized as inputs for the subsequent layer, where an integrated safety index is inferred. Ultimately, the safety index is generated as the final outcome. The system's results are conveyed through warnings and an error measurement on a safety scale ranging from 1 to 10. Furthermore, web service is developed to allow the construction management to check the worker safety condition of the construction site in real-time, while also monitoring the operational status of the IoT devices, allowing for the early detection of sensor malfunction and the subsequent guarantee of worker safety. Extensive evaluations conducted to test the performance of the developed framework verify its efficiency to provide improved risk assessment, real-time monitoring, and proactive safety actions, encouraging a safer and more productive work environment.

A New Method for Determination of Optimal Borehole Drilling Location Considering Drilling Cost Minimization and Sustainable Groundwater Management.

Drilling boreholes for the exploration of groundwater incurs high cost with potential risk of failures. However, borehole drilling should only be done in regions with a high probability of faster and easier access to water-bearing strata, so that groundwater resources can be effectively managed. However, regional strati-graphic uncertainties drive the decision of the optimal drilling location search. Unfortunately, due to the unavailability of a robust solution, most contemporary solutions rely on physical testing methods that are resource intensive. In this regard, a pilot study is conducted to determine the optimal borehole drilling location using a predictive optimization technique that takes strati-graphic uncertainties into account. The study is conducted in a localized region of the Republic of Korea using a real borehole data set. In this study we proposed an enhanced Firefly optimization algorithm based on an inertia weight approach to find an optimal location. The results of the classification and prediction model serve as an input to the optimization model to implement a well-crafted objective function. For predictive modeling a deep learning based chained multioutput prediction model is developed to predict groundwater-level and drilling depth. For classification of soil color and land-layer a weighted voting ensemble classification model based on Support Vector Machines, Gaussian Naïve Bayes, Random Forest, and Gradient Boosted Machine is developed. For weighted voting, an optimal set of weights is determined using a novel hybrid optimization algorithm. Experimental results validate the effectiveness of the proposed strategy. The proposed classification model achieved an accuracy of 93.45% and 95.34% for soil-color and land-layer, respectively. While the mean absolute error achieved by proposed prediction model for groundwater level and drilling depth is 2.89% and 3.11%, respectively. It is found that the proposed predictive optimization framework can adaptively determine the optimal borehole drilling locations for high strati-graphic uncertainty regions. The findings of the proposed study provide an opportunity to the drilling industry and groundwater boards to achieve sustainable resource management and optimal drilling performance.

Quantitative Assessment of the Portal Pressure for the Liver Surgery Using Serological Tests.

OBJECTIVE: To establish a reliable equation to predict hepatic venous pressure gradient (HVPG) using serological tests for surgical patients with hepatocellular carcinoma (HCC). BACKGROUND: Accurate assessment of portal pressure for surgical patients with HCC is important for safe hepatic resection (HR). The HVPG is regarded as the most reliable method to detect portal hypertension. However, HVPG is not utilized in many medical centers due to invasiveness of procedure. METHODS: Between 2006 and 2008, 171 patients (Correlation cohort), who underwent liver surgery in a tertiary hospital, were enrolled. Preoperative measurements of the HVPG and serological tests were performed simultaneously. Correlation between the HVPG and serological tests were analyzed to establish an equation for calculated HVPG (cHVPG). Between 2008 and 2013, 510 surgical patients (Application cohort) were evaluated, and HR recommended when cHVPG < 10 mm Hg. The outcomes of HR were analyzed to evaluate reliability of the cHVPG for HR. RESULTS: In the correlation cohort, the equation for cHVPG was established using multivariate linear regression analysis; cHVPG (mm Hg) = 0.209 × [ICG-R15 (%)] - 1.646 × [albumin (g/dL)] - 0.01×[platelet count (10)] + 1.669 × [PT-INR] + 8.911. In the application cohort, 425 patients with cHVPG < 10 mm Hg underwent HR. Among them, 357 had favorable value of ICG-R15 < 20% (group A), and 68 had unfavorable value of ICG-R15 ≥ 20% (group B). There was no significant difference in patient demographics, tumor characteristics, operative outcome, and survival rates between group A and B. CONCLUSIONS: The equation for cHVPG of this study was established on statistical reliability. The cHVPG could be useful to predict portal pressure quantitatively for surgical patients with HCC using serological tests.