How do you use field observations and post-disaster investigations in your design?

1 Field observations

Field observations are the systematic collection and analysis of data from the site of a structure before, during, or after an extreme event. They can help you to understand the environmental conditions, the structural response, the failure mechanisms, and the human factors that influence the performance of your design. You can use field observations to verify your assumptions, to calibrate your models, to validate your codes and standards, and to assess the adequacy of your design criteria.

2 Post-disaster investigations

Post-disaster investigations are the in-depth studies of the damage and the recovery of structures after an extreme event. They can help you to determine the extent and severity of the damage, to identify the root causes and the contributing factors, to evaluate the effectiveness of the mitigation and the repair measures, and to estimate the social and economic impacts of the event. You can use post-disaster investigations to learn from the failures, to improve your design methods, to update your codes and standards, and to enhance your design resilience.

3 Data collection methods

To conduct field observations and post-disaster investigations, you need to use data collection methods that are suitable for your objectives, resources, and constraints. Visual inspection, measurement, testing, sampling, and interviewing are all common methods. With visual inspection, you can observe the physical condition of the structure and its components. Measurement involves using instruments to measure dimensions and forces. Testing involves using tools to test the strength of the structure and its components. Sampling involves collecting samples of materials and soils. And interviewing involves collecting information from stakeholders and users of the structure and its event.

4 Data analysis methods

To make sense of the data collected from field observations and post-disaster investigations, you need to use various data analysis methods that are appropriate for your data type, quality, and quantity. Descriptive analysis, for example, involves summarizing, organizing, and presenting data in a clear and concise way with tables, charts, graphs, maps, and statistics. Comparative analysis is another method that uses ratios, percentages, trends, and benchmarks to compare the data with other sets. Inferential analysis utilizes regression, correlation, hypothesis testing, and confidence intervals to infer the relationships between the data and the performance of your design. Lastly, predictive analysis involves simulation, optimization, risk analysis, and scenario analysis to predict the future performance of your design under different conditions.

5 Design implications

When applying the results of field observations and post-disaster investigations to your design, you should consider the implications for your design objectives, criteria, methods, and solutions. This could involve revising objectives to reflect actual needs, adjusting criteria to account for environmental conditions, improving methods to incorporate the latest knowledge and technologies, and modifying solutions to implement best practices. Ultimately, this will help you enhance design accuracy, efficiency, reliability, safety, functionality, and resilience.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?