Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks.

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera's pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Monitoring offshore oil pollution using multi-class convolutional neural networks.

Oil and gas production operations are a major source of environmental pollution that expose people and habitats in many coastal communities around the world to adverse health effects. Detecting oil spills in a timely and precise manner can help improve the oil spill response process and channel required resources more effectively to affected regions. In this research, convolutional neural networks, a branch of artificial intelligence (AI), are trained on a visual dataset of oil spills containing images from different altitudes and geographical locations. In particular, a VGG16 model is adopted through transfer learning for oil spill classification (i.e., detecting if there is oil spill in an image) with an accuracy of 92%. Next, Mask R-CNN and PSPNet models are used for oil spill segmentation (i.e., pixel-level detection of oil spill boundaries) with a mean intersection over union (IoU) of 49% and 68%, respectively. Lastly, to determine if there is an oil rig or vessel in the vicinity of a detected oil spill and provide a holistic view of the oil spill surroundings, a YOLOv3 model is trained and used, yielding a maximum mean average precision (mAP) of ~71%. Findings of this research can improve the current practices of oil pollution cleanup and predictive maintenance, ultimately leading to more resilient and healthy coastal communities.

Factors affecting unsafe behavior in construction projects: development and validation of a new questionnaire.

Introduction. Occupational safety in general, and construction safety in particular, is a complex phenomenon. This study was designed to develop a new valid measure to evaluate factors affecting unsafe behavior in the construction industry. Methods. A new questionnaire was generated from qualitative research according to the principles of grounded theory. Key measurement properties (face validity, content validity, construct validity, reliability and discriminative validity) were examined using qualitative and quantitative approaches. The receiver operating characteristic curve was used to estimate the discriminating power and the optimal cutoff score. Results. Construct validity revealed an interpretable 12-factor structure which explained 61.87% of variance. Good internal consistency (Cronbach's α = 0.94) and stability (intra-class correlation coefficient = 0.93) were found for the new instrument. The area under the curve, sensitivity and specificity were 0.80, 0.80 and 0.75, respectively. The new instrument also discriminated safety performance among the construction sites with different workers' accident histories (F = 6.40, p < 0.05). Conclusion. The new instrument appears to be a valid, reliable and sensitive instrument that will contribute to investigating the root causes of workers' unsafe behaviors, thus promoting safety performance in the construction industry.

A qualitative investigation of factors influencing unsafe work behaviors on construction projects.

BACKGROUND: Unsafe behavior is an important component in the chain of accident occurrences, and thus plays a key role in the accident prevention programs in construction sites. OBJECTIVE: The aim of this qualitative research is to study the perception of frontline workers, supervisors, and managers about the preconditions of and contributing factors to unsafe behaviors in civil engineering projects. METHODS: Field observation, in-depth interview, and focus group discussion are conducted with 113 informants from various mega projects during a 2-year time period. Fishbone diagram is applied to describe a conceptual model. RESULTS: The results point to fourteen themes within four categories of the conceptual model - general management, organizational factors, safety supervision and management, and individual characteristics. CONCLUSIONS: General management and organizational culture were introduced as important preconditions and contributing factors resulting in human error and unsafe behavior in the construction sites. The fishbone diagram reveals the sequence and interaction of preconditions and contributing factors. The key contributing factors and their influences on unsafe behaviors are discussed along with recommendations for future directions.

Ergonomic analysis of construction worker's body postures using wearable mobile sensors.

Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.

Factors influencing unsafe behaviors and accidents on construction sites: a review.

OBJECTIVE: Construction is a hazardous occupation due to the unique nature of activities involved and the repetitiveness of several field behaviors. The aim of this methodological and theoretical review is to explore the empirical factors influencing unsafe behaviors and accidents on construction sites. METHODS: In this work, results and findings from 56 related previous studies were investigated. These studies were categorized based on their design, type, methods of data collection, analytical methods, variables, and key findings. A qualitative content analysis procedure was used to extract variables, themes, and factors. In addition, all studies were reviewed to determine the quality rating and to evaluate the strength of provided evidence. RESULTS: The content analysis identified 8 main categories: (a) society, (b) organization, (c) project management, (d) supervision, (e) contractor, (f) site condition, (g) work group, and (h) individual characteristics. The review highlighted the importance of more distal factors, e.g., society and organization, and project management, that may contribute to reducing the likelihood of unsafe behaviors and accidents through the promotion of site condition and individual features (as proximal factors). CONCLUSION: Further research is necessary to provide a better understanding of the links between unsafe behavior theories and empirical findings, challenge theoretical assumptions, develop new applied theories, and make stronger recommendations.

Structural modeling of safety performance in construction industry.

BACKGROUND: With rapid economic development and industrialization, the construction industry continues to rank among the most hazardous industries in the world. Therefore, construction safety is always a significant concern for both practitioners and researchers. The objective of this study was to create a structural modeling of components that influence the safety performance in construction projects. METHODS: We followed a two-stage Structural Equation Model based on a questionnaire study (n=230). In the first stage, we applied the Structural Equation Model to the proposed model to test the validity of the observed variables of each latent variable. In the next stage, we modified the proposed model. The LISREL 8.8 software was used to conduct the analysis of the structural model. RESULTS: A good-fit structural model (Goodness of Fit Index=0.92; Root Mean Square Residual=0.04; Root Mean Square Error of Approximation=0.04; Comparative Fit Index=0.98; Normalized Fit Index=0.96) indicated that social and organizational constructs influence safety performance via the general component of the safety climate. CONCLUSION: The new structural model can be used to provide better understanding of the links between safety performance indicators and contributing components, and make stronger recommendations for effective intervention in construction projects.