Corrosion of steel reinforcement in concrete is one of the main issues plaguing aging infrastructure worldwide. While concrete’s carbonation is usually associated with an increased risk of corrosion, this study proposes a counterintuitive method that uses carbonation of hardened elements to reduce corrosion risk: the induced rapid surface carbonation (IRS-C) treatment. Besides, this study analyzes the impact of IRS-C on the corrosion resistance of precast reinforced concrete elements and explains the mechanisms behind it. The IRS-C treatment consists of placing the hardened samples in a vacuum chamber, filling it with CO2 after removing the air, and leaving them for 48h, which induces rapid carbonation of the samples' outer surfaces. 12 concrete beams, 8 with and 4 without reinforcement, and 12 cylinders were used. Half of the samples were treated with IRS-C, while the remaining served as a reference. ASTM G109 results showed that the IRS-C treated samples had lower total corrosion than the untreated samples. The phenolphthalein test confirmed that the treatment did not affect pH near the reinforcement and therefore did not lower the chloride threshold for corrosion. Furthermore, water absorption and titration tests confirmed that IRS-C formed a low-porosity surface layer that reduced water and chloride penetration. Thus, the IRS-C treatment is a promising method to improve the corrosion resistance of precast reinforced concrete elements.

Launching and landing pads (LLPs) will be essential elements to serve a future lunar base. There is widespread acceptance that these structures will be manufactured from in situ materials. Structural materials made from these indigenous materials are expected to be brittle. Thus, they will exhibit the typical diverse behaviors and inherent variabilities in properties that are common in indigenous construction materials. Hard-won lessons on how to design reliable structures from brittle materials on the Earth need to be leveraged. It should be noted that earlier examples of these applications resulted in massive structures. It was not until Portland cements, other cementitious materials, chemical admixtures, steel, and more recently carbon fiber-based elements became available, and in general, composites were better understood, that more elegant and slender structures were realized. In this research, we adapt and apply well-established methodologies used for slab-on-grade construction toward the development of a design framework for lunar LLPs. The structural considerations in designing such a slab are discussed in detail starting from first principles and unknown initial dimensions to illustrate their impact on the design. This approach is necessary given the low state of entropy of the knowledge with respect to performance and actual material properties in such extreme environments. It is demonstrated through an illustrative example in which we design an LLP using the best available information and properties for sintered lunar regolith. Additional information that will need to be obtained or verified through in situ testing is also defined. The example design is intended to service spacecraft of up to 50 tons. The dimensions used for the loads represent spacecraft being developed at the present time for transporting cargo and supplies to the surface of the Moon. The application of a wealth of fundamental knowledge, practical experience, and technologies will be essential for the design and construction of resilient and sustainable infrastructure on the Moon and other similarly challenging environments. The paper concludes with a discussion of the path forward to design and realize such construction on the Moon.

Structural drawings are widely used in many fields, e.g., mechanical engineering, civil engineering, etc. In civil engineering, structural drawings serve as the main communication tool between architects, engineers, and builders to avoid conflicts, act as legal documentation, and provide a reference for future maintenance or evaluation needs. They are often organized using key elements such as title/subtitle blocks, scales, plan views, elevation view, sections, and detailed sections, which are annotated with standardized symbols and line types for interpretation by engineers and contractors. Despite advances in software capabilities, the task of generating a structural drawing remains labor-intensive and time-consuming for structural engineers. Here we introduce a novel generative AI-based method for generating structural drawings employing a large language model (LLM) agent. The method incorporates a retrieval-augmented generation (RAG) technique using externally-sourced facts to enhance the accuracy and reliability of the language model. This method is capable of understanding varied natural language descriptions, processing these to extract necessary information, and generating code to produce the desired structural drawing in AutoCAD. The approach developed, demonstrated and evaluated herein enables the efficient and direct conversion of a structural drawing's natural language description into an AutoCAD drawing, significantly reducing the workload compared to current working process associated with manual drawing production, facilitating the typical iterative process of engineers for expressing design ideas in a simplified way.

Space exploration is progressing toward long-term missions that involve both human (HAs) and robotic agents (RAs) in operations in lunar space habitats, the Gateway space station, and the moon-to-Mars program. These missions require high-level intelligence and a sustained performance over extended periods. Analyzing agent performance solely at the task level is insufficient for such complex applications because the resources consumed by agents are coupled with the utility they provide under various conditions. Additionally, factors such as the availability of agents to respond to hazardous events, impacted by factors including human sleep cycles and robot charging times, must be considered. Understanding how resources, utility, and availability are interrelated is crucial for early-phase decision making, assessing logistics, and steering investments in promising directions. In this study, the rapid simulation capabilities of control-oriented dynamic computational modeling (CDCM) were used to explore the trade space involving an HA and an RA tasked with maintaining a smart space habitat. This approach was used to model two independent parallel scenarios as systems of systems that use stochastic methods to account for mission variabilities. A human scientist (HS) was included to quantify the mission’s research outcomes. The outcomes generated by the HS served as a metric to compare the performance of the agents along with the costs associated with engaging the HA and RA.

A Network Coordination Office (NCO) is at the core of the Natural Hazards Engineering Research Infrastructure (NHERI), a national, 12-component, distributed research network, funded by the National Science Foundation (NSF). NHERI is focused on research that both mitigates damage and increases resilience from natural hazards such as hurricanes and other extreme windstorms, storm surge, tsunami waves, and earthquakes. NCO activities engage all facilities within NHERI, uniting the network’s four diverse component types comprised of experimental facilities, a cyberinfrastructure for data and computing resources, a center for the creation of modeling and simulation tools, and a repository of equipment, software and support for rapid reconnaissance. Outcomes from NCO governance activities include two network-wide summits, five international partnerships, a central scheduling tool, and a means for external evaluation. The NCO’s education and community outreach has established an extremely successful pipeline for engineering education from elementary and secondary educators to undergraduates, graduate students, and early career faculty. The NCO conducts centralized communication activities such as newsletter publication, e-mail announcements, podcasts, and social media engagements that unite the natural hazards research community and amplify NHERI’s impact. Led by the NCO, the NHERI Science Plan presents a long-term vision for the natural hazards research community and serves as a roadmap for future high-impact, high-reward, hazards engineering and interdisciplinary research at NHERI facilities. The NCO also promotes technology transfer through education and one-on-one engagement with researchers. Overall, the NCO unifies and strengthens the research network through its variety of initiatives, amplifying the impact of this multifaceted NSF research network and provides a template for the management of large, distributed research networks.

The design of lunar habitats demands a non-traditional approach due to the constraints imposed by the extreme environment in deep space and the limited launch mass available for NASA missions. The potential damage caused by micrometeorite impacts, a natural hazard to lunar habitats, will generate high power demands to maintain stable thermal conditions within the habitat and require the allocation of dedicated resources for conducting repair actions. Locally available solidified regolith and high-strength and low-density transported materials represent two viable options for lunar habitat construction. This study assessed the performance of different structural configurations for a semi-spherical dome, which involved varying materials and thickness, by comparing the power consumption and the time required for repairs due to impact damage over a 20-year service period. The trade study was performed under the recently developed control-oriented dynamic computational modeling (CDCM) framework, a modular platform for modeling space habitats. To conduct this analysis, it was essential to quantify the perforation damage depth after every micrometeorite impact for regolith and aluminum targets. Hence, within the CDCM, a polynomial equation was utilized to predict the perforation depth of micrometeorite impacts with varying diameters and velocities. The study showed that if a regolith and aluminum habitat are constructed with the same equivalent mass and exposed to the same micrometeorite impact, the regolith habitat will experience a higher perforation depth than the aluminum habitat. This behavior led to higher repair time demands in regolith habitats. Moreover, regolith structures exhibited more significant performance uncertainty under micrometeorite impacts than aluminum structures with the same equivalent mass; however, they consume less power during their lifecycle. While the evaluation relies on the assumptions employed in regolith modeling, the study demonstrates that CDCM is an effective tool for assessing the potential performance of habitat designs over an extended operational lifespan.

This research examines how ductility affects the durability of lunar surface structures against recurring disturbances like moonquakes, micrometeorite impacts, and thermal cycles over an extended period. The structural performance at various levels of ductility was determined by adjusting material parameters and the thickness of a reference multilayered dome structure. Moonquake and micrometeorite impact-induced lateral displacements were estimated using a reduced-order model under a control-oriented dynamic computational modeling framework. The study considered the degradation of the metallic dome's strength properties over time due to thermal cycles. Fragility curves were generated by assessing the likelihood of reaching three predefined damage levels as a result of multiple hazards. Additionally, a discounted cash flow analysis was conducted to incorporate a financial aspect into the performance comparison. The findings revealed that structures with sufficient ductility capacity have a lower probability of sustaining severe damage or collapsing within a shorter time frame. Hence, having ductile structures in lunar environments is advantageous as it allows the postponement of maintenance and repair actions, thereby conserving scarce resources for more urgent tasks. Moreover, the financial analysis demonstrated that lunar habitats with higher ductile capacities result in larger net present values, offering a higher return on the initial investment.

The COVID-19 era has witnessed numerous successful and unsuccessful attempts to adapt or reconfigure physical, virtual, and hybrid aspects of the built environment in order to mitigate the risks of co-occuring (i.e., compound) hazards. But it has also witnessed major challenges to ensuring that the protections these reconfigurations afford are equitably distributed. Additional theoretical and empirical research is needed to inform transitions (via adaptive reconfiguration) toward short-term goals of health and well-being, as well as to guide transformations (via the establishment of stable configuration) toward longer-term goals of equitable societal function. To this end, this paper presents a framework for conceptualizing adaptation of the built environment as a series of state transitions in response to (or in anticipation of) compound hazards. It draws upon cases from recent experience in the areas of food production, shelter, and education to critique, clarify, and explicate this framework. It concludes with implications for further research on the management of transitions in the built environment under a range of hazard scenarios. The COVID-19 pandemic provided a global backdrop for the study of the capacities and vulnerabilities of many aspects of societal function, challenging conventions around the design and operation of wide classes of infrastructure to protect populations from pandemics as well as hazards such as hurricanes and earthquakes. The framework offered by this study, and its application through the associated case studies, reveals how observed adaptations of the built environment can elucidate new potentials to mitigate the risks associated with co-occuring (i.e., compound) hazards, as well as areas where our existing conventions are no longer compatible with contemporary uses of the built environment. One such convention challenged by this study is the definition of critical infrastructure in existing regulatory frameworks. The built environment transitions documented by this study suggest that contemporary notions of what infrastructure is critical and what services are essential have outstripped the traditional notions in codes and standards, demanding corresponding realignment of regulatory frameworks to ensure life-safety can still be achieved as usages evolve.

The pursuit of knowledge and curiosity are once again driving humanity to seek a human presence on extraterrestrial bodies. While numerous challenges must be overcome on the moon to achieve this goal, the moonquake hazard has often been overlooked. Using NASA’s Moon to Mars Architecture requirements for surface habitation and accounting for the midterm/long-term mission duration, a framework for vulnerability assessment is developed for essential nonstructural elements (NSEs) inside an inflatable habitat subjected to moonquakes. The aim of this study is to develop an approach to assess the vulnerability of typical NSEs that support essential equipment, such as the environmental control and life support system, under a paucity of information related to the seismic hazard. We also emphasize that the launch dynamic environment and lunar seismic environment exhibit notable differences in their characteristics, which can lead to higher lateral acceleration levels than initially expected. This acceleration may be linked to high-occurrence seismic events. However, due to the lack of information regarding the frequency of these events, the risk of NSEs being subjected to unforeseen loads increases. It is imperative to equip future lunar habitats with seismic mitigation measures until additional seismometers are deployed near upcoming surface human outposts.

Lunar structures will be exposed to one of the most extreme environments that have ever been considered for human settlements. In situ, regolith-based materials are being proposed for construction on the moon, offering the benefit of reducing the cost of transporting large amounts of materials or prefabricated elements, and relying on the ability to transport mainly the equipment needed to construct landing pads, shelters, blast shields, habitats, roadways, etc. However, the properties of materials that are made, all or in part, from indigenous lunar resources are likely to change based on the make-up of the material, the location where it was taken from, the production processes, and time. No standards or building codes exist for the design and construction of infrastructure on the moon. Engineers will need dependable information about these materials before any design can be completed. Hard-won lessons from centuries of using similar resources on Earth need to be leveraged to develop the best procedures that will be critical for testing such materials for structural applications. Here we discuss the technical challenges of establishing such standards. Using the timely example of a landing pad on the moon, we identify the gaps in both knowledge and testing capabilities that exist today.

This paper presents a novel framework for assessing the impact of construction defects on bridge deck corrosion-induced deterioration. The model developed quantifies the impact of insufficient concrete cover, damage to the rebar epoxy coating, improper curing technique, and excessive water-cement ratio on the lifespan of concrete bridge decks. Regional variations, including freeze-thaw cycles, carbonation, and deicing salt usage, are also considered in the model. Two applications for estimating deterioration and decrease of condition rating are presented. Simulation results using the model developed reveal a substantial loss of life when these defects occur. This work highlights the importance of proper construction practices in ensuring long-term infrastructure integrity. Moreover, it provides a valuable tool for assessing the impact of these common construction defects on bridge deck deterioration. This tool enables bridge managers to make better-informed decisions regarding repair operations and ask contractors for fairer compensations when defective practices are observed during construction.

The establishment of secure earth-independent long-term lunar habitats has been envisioned by numerous government agencies and private companies. Recent advancements in assessing seismic hazards caused by shallow moonquakes have highlighted the importance of incorporating this phenomenon into the design of robust and resilient lunar structures. However, further research is required to explore lunar habitat design that considers seismic loads. This paper proposes assessing the structural response of a lunar habitat made of sulfur concrete covered with a regolith layer. The numerical model of the structure is subjected to gravitational, internal pressure and seismic loads. The seismic analysis of the structure is carried out using spectral and nonlinear time history methods. Conditional mean spectra for shallow moonquakes with return periods of 75, 475, 970 and 2475 years are used in the seismic analysis. The records used for the temporal analyses were ground motions that agree with a preliminary seismic hazard on the Moon. The results of temporal analyses reveal that shallow moonquakes with return periods greater than 475 years can lead to the loss of the global stability of the structure. Consequently, the findings imply that seismic loads have the potential to impose unacceptable demands on lunar structures constructed from in-situ materials like sulfur concrete. Hence, it is imperative to incorporate seismic considerations in the design process for developing resilient and long-term lunar habitats.

The dynamics of systems of systems often involve complex interactions among the individual systems, making the implications of design choices challenging to predict. Design features in such systems may trigger unexpected behaviors or result in large variations in safety, performance or resilience. To provide a means of simulating such systems for aiding in these decisions, we have developed a prototype tool, the control-oriented dynamic computational modeling tool (CDCM). The CDCM provides rapid simulation capabilities to perform trade studies in systems of systems. The general class of systems of systems that we aim to examine involve multiple hazards, damage, cascading consequences, repair and recovery. We especially focus on systems-of-systems that incorporate a health management system (HMS) that can monitor the state of the habitat and make decisions about actions to take. In this paper we describe the features of the CDCM, the architecture we devised for simulation of systems-of-systems, the unique functionalities of this tool, and we provide a demonstration of the capabilities by performing two illustrative examples. We articulate the use of this tool for making early design decisions and demonstrate its use for trade studies that consider a model of a deep space habitat. We also share some experiences and lessons that may be useful for others seeking to address similar problems.

The spirit of exploration and pursuit of knowledge are once again driving humanity to seek a human presence on extraterrestrial bodies. While numerous challenges must be overcome to achieve this goal, the existence of moonquakes has often been overlooked. The Apollo Passive Seismic Experiment (APSE) collected 8 years' worth of data, offering the scientific community insights into the Moon's internal mechanisms. Using NASA's Moon to Mars Architecture requirements for surface habitation, a procedure for vulnerability assessment is developed herein for essential non-structural elements (NSE) inside an inflatable habitat. The NSE element used for this study is the environmental control and life support system (ECLSS). The aim of this study is to propose an approach to assess the vulnerability of the NSE that supports essential equipment under a paucity of information related to the seismic hazard. We also emphasize herein that the launch dynamic environment behaves differently, yielding a need to equip a future habitat with seismic mitigation measures. The findings in this work suggest that due to epistemic uncertainty, significant disturbances may result in long- lasting vibrations that need to be considered as they may require effective mitigation measures.

Summary The automated identification of building characteristics for seismic vulnerability remains a challenge for governments due to the high number of buildings in cities. The diverse architectural styles of these buildings complicate the automated identification of building information (e.g., number of stories, structural system, and material type). Deep learning techniques lose accuracy as they generalize information, while the visual contents of a building exhibit a considerable range and diversity. This study leverages the pose detection technique to tackle such issues by focusing on a common construction style: reinforced concrete buildings representing columns, beams, or floors on the façade. With an aim to enable the assessment of seismic vulnerability, the technique developed herein is conceived for buildings with up to six stories that are more likely to be moment‐frame buildings. The AI‐enabled proposed framework starts with collecting building images and categorizing those containing this specific building type. A bounding box detector is then used to isolate building facades, for the subsequent identification of the structural frame with the High‐Resolution Network (HR‐Net). For demonstration, we illustrate this technique by identifying the structural frame on concrete buildings with a sample dataset developed based on buildings found in Mexico City in a pre‐earthquake event state.

With the overwhelming number of older reinforced concrete buildings that need to be assessed for seismic vulnerability in a city, local governments face the question of how to assess their building inventory. By leveraging engineering drawings that are stored in a digital format, a well-established method for classification reinforced concrete buildings with respect to seismic vulnerability, and machine learning techniques, we have developed a technique to automatically extract quantitative information from the drawings to classify vulnerability. Using this technique, stakeholders will be able to rapidly classify buildings according to their seismic vulnerability and have access to information they need to prioritize a large building inventory. The approach has the potential to have significant impact on our ability to rapidly make decisions related to retrofit and improvements in our communities. In the Los Angeles County alone it is estimated that several thousand buildings of this type exist. The Hassan index is adopted here as the method for automation due to its simple application during the classification of the vulnerable reinforced concrete buildings. This paper will present the technique used for automating information extraction to compute the Hassan index for a large building inventory.

To construct and operate a deep space habitat, for example, on Mars, is an ambitious goal but bound to happen for future space exploration. This mission represents a grand challenge that will require the application of the highest technologies we have. Countless questions are to be answered. One of the most challenging questions is the roles of humans and instead of/or robots to operate the space habitat? There may not be a definite and decisive answer to this problem in the short term depending on the critical technologies involved in human life support and robotic autonomy. However, by quantitatively comparing a human and a robot for designated mission tasks, evidence can be found to support later strategy and to encourage research in directions relating automation and human machine interaction. For such purposes, two independent parallel scenarios, in this paper, are formed to compare the mission success with a human agent (HA) and a robot agent (RA). In each scenario, HA and RA are scheduled appropriately to carry out a series of tasks to maintain the space habitat in a safe and functional status. The tasks include to repair subsystems, e.g., power, structure, etc. in terms of reacting to emergencies. Meanwhile, the default daily activities of HA and RA are also modelled, e.g., sleeping of HA, recharging of RA, etc. Most importantly, to evaluate the actual performance of HA and RA, we have included an independent research scientist in the study. The research scientist, of which the model inherits from HA, exists in both the scenarios for HA and RA, and is solely to generate research outcome. The outcome generate by the research scientist is the metric utilized to compare the performance of the agents, besides the equivalent costs to engage HA and RA.

Departments of transportation (DOTs) throughout the United States maintain vast bridge databases that house information such as bridge services, dimensions, materials, inspection reports, and photographs. These databases are expensive to maintain and have evolved quite gradually over the years. They are meant to be substantial enough, at a bare minimum, to support typical asset management activities and to prioritize maintenance tasks. There is great potential to make use of them to support other decisions. However, these databases often lack certain detailed information related to substructure elements, which is necessary for seismic vulnerability assessment, for example, and would be time-consuming to gather for thousands of bridges in a given region or state. In this study, a technique was demonstrated and validated that reduces the time needed to collect this information, by leveraging artificial intelligence to automate the identification of substructure types using images. We defined categories appropriate for vulnerability assessment task, classifiers were trained to identify visual content, and their performance evaluated. In this paper we illustrate a method to determine whether to use artificial intelligence, human visual confirmation, or a combination of the two, to identify bridge substructure types based on accuracy, cost, and risk tolerance. The technical approach was validated using images from Indiana. This leveraging of artificial intelligence for automated identification of critical bridge characteristics from readily available images could empower asset owners, such as DOTs, to assess their inventory more frequently and with confidence.

The purpose of a routine bridge inspection is to assess the physical and functional condition of a bridge according to a regularly scheduled interval. The Federal Highway Administration (FHWA) requires these inspections to be conducted at least every 2 years. Inspectors use simple tools and visual inspection techniques to determine the conditions of both the elements of the bridge structure and the bridge overall. While in the field, the data is collected in the form of images and notes; after the field work is complete, inspectors need to generate a report based on these data to document their findings. The report generation process includes several tasks: (1) evaluating the condition rating of each bridge element according to FHWA Recording and Coding Guide for Structure Inventory and Appraisal of the Nation’s Bridges; and (2) updating and organizing the bridge inspection images for the report. Both of tasks are time-consuming. This study focuses on assisting with the latter task by developing an artificial intelligence (AI)-based method to rapidly organize bridge inspection images and generate a report. In this paper, an image organization schema based on the FHWA Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation’s Bridges and the Manual for Bridge Element Inspection is described, and several convolutional neural network-based classifiers are trained with real inspection images collected in the field. Additionally, exchangeable image file (EXIF) information is automatically extracted to organize inspection images according to their time stamp. Finally, the Automated Bridge Image Reporting Tool (ABIRT) is described as a browser-based system built on the trained classifiers. Inspectors can directly upload images to this tool and rapidly obtain organized images and associated inspection report with the support of a computer which has an internet connection. The authors provide recommendations to inspectors for gathering future images to make the best use of this tool.