Smart buildings can sense hazards, but most still depend on human intervention and lack safety-driven autonomous response. We present ABCD-Active Buildings with Computing and Distributed Sensing, a framework that embeds sensing, edge intelligence, and actuation directly into building surfaces, enabling real-time mitigation. ABCD decisions follow the HHU Laws, which enforce a hierarchy of occupant safety, secure inter-zone collaboration, and structural protection. We built a Raspberry Pi-based prototype that demonstrated autonomous flame detection and water-based suppression in controlled indoor experiments, achieving a 97% success rate and zero false activations. The system includes authenticated low-latency alert exchange between zones, occupancy-aware suppression logic, and a path toward runtime assurance to formalize HHU safety rules. Together, these results show how embedded autonomy and ethical control can transform passive structures into resilient, self-protective building systems.

To achieve a lightweight and high-stiffness design of robotic arms for construction robots, this paper proposes a novel data-driven generative design framework that integrates topology optimization, 3D generative adversarial networks (GANs), reverse engineering, and additive manufacturing. This paper develops a three-dimensional deep generative model that integrates Topology Optimization (TO) with Generative Adversarial Networks (GANs) to result in more efficient outcomes. A high-fidelity CAD model of a construction robotic arm was first established, from which a diverse geometric dataset containing 350 topology optimized models was generated. Subsequently, a 3D-GAN architecture was trained on this dataset to synthesize high-performance voxelized structural configurations, which were then transformed into smooth solid models via a streamlined reverse engineering workflow. Finally, the representative joint model achieved a mass reduction of approximately 51.67% and 28.93% compared to the initial model and topological model, while maintaining mechanical performance. Furthermore, physical prototypes were successfully fabricated using 3D printing technology, confirming the geometric compliance and manufacturability of the generated models. This work demonstrates the feasibility and effectiveness of combining generative deep learning with computational topology optimization and additive manufacturing for the intelligent design of construction robot components.

Bridges are key components of transportation networks around the world. Millions of bridges were constructed over the past centuries. However, many of these structures are aging or facing extreme events. Current inspection methods are reliant on manual labor, which only makes them costly, inefficient, and outdated. These current approaches lack a clear system for optimally inspecting the bridge, obtaining better information about its condition. To address this challenge, this paper presents a novel framework guided by Bayesian optimization for detecting bridge local damage using sensors attached to Unmanned Aerial Vehicles (UAVs). A cost-effective and practical Utility Function Framework (UFF) method is developed, which leverages orchestrated UAVs with optimum spacing. To rigorously validate the approach, a validation analysis is conducted by exhaustively testing all possible sensor placements on a 10 m bridge, providing a theoretical baseline against which the proposed method is evaluated. Additionally, a field case study validated the proposed method of sensor placement. The framework is validated as a proof-of-concept using a 10 m numerical benchmark and a field-inspired case study, and it is intended to demonstrate feasibility rather than universal performance across all bridge typologies. While the study focuses on a single span with a controlled damage scenario, the results establish feasibility and motivate future validation on longer spans, varied bridge typologies, and practical field investigations.

Reciprocal structures represent a category of spatial structural systems characterized by mutually supporting connections between components, which are primarily employed in roof systems and spatial frameworks. Insufficient stiffness is a critical challenge for the practical application of reciprocal structures in engineering. This study introduces a new cable-supported reciprocal structure, which integrates rigid beams and flexible cables through struts, inspired by the construction principles of beam string structures. By optimizing the force transfer path, the overall performance of the structure is improved. Through finite element analysis, a comprehensive investigation was conducted on the static behavior and parametric analysis of this new reciprocal structure. The research results show that the rational determination of key parameters, including the rise/span ratio, strut height, and cable length, demonstrates significant efficacy in reducing the maximum displacement of this new reciprocal structure under uniformly distributed load. The stiffness of the new cable-supported reciprocal structure increases by 17% compared to that of the general reciprocal structure, while the total steel consumption decreases by 12%. The proposed new cable-supported reciprocal structure presents an innovative and viable configuration within the domain of reciprocal structures.

During a building fire, occupants need to find an escape route free from smoke and fire hazards. However, fire and smoke can block signs and hallways that become impossible to navigate, making evacuation difficult. On the other hand, rescuers often lack real-time information about people's locations, which increases rescue time as they manually search for survivors. To resolve this, we present RESCUE - an evacuation support system that pairs smartwall based hazard sensors with a lightweight mobile interface to deliver a live floor map and an up-to-date shortest safe route to the nearest stairs in buildings. We represent the building graph, which updates in real-time from smart wall embedded sensors. When the smart wall detects fire, smoke, or gas in a corridor segment, that segment is immediately blocked and excluded from routing. The occupant's mobile app refreshes to display the shortest, safest route to the nearest stair/exit. Responders also view a live display of occupant locations from the app, enabling them to go directly to the correct floor and corridor, rather than checking every floor. We implemented a prototype on one floor of a campus building and simulated a five-floor deployment using the measured plan. Our RESCUE system reduced time to exit versus static signage by avoiding trial and error detours. These results indicate that real-time, sensor aware, map guided guidance improves both occupant self evacuation and responder effectiveness during building emergencies.

Bridge infrastructures face challenges from aging, overloading, and environmental hazards, making structural health monitoring (SHM) crucial for functionality, safety, and longevity. Conventional SHM techniques are limited by intrusive sensors and restricted coverage. With advancements in vision-based techniques, digital image correlation (DIC) has emerged as a non-contact method capable of full-field displacement, strain, and crack monitoring. This review reveals a growing research interest in the application of DIC for bridge SHM, particularly accelerating after 2020. The key DIC applications involve crack detection, modal identification, displacement tracking, and fatigue assessment. However, challenges persist, including calibration sensitivity, environmental influence, and limited real-time and full-structure abilities. To overcome these, a roadmap is proposed integrating DIC with Internet of Things (IoT), drone-based imaging, energy-efficient algorithms, advanced vision models, and digital twins for intelligent SHM. This integration supports safer, smarter, and more resilient bridge infrastructure systems.

Bridges are vital components of global infrastructure, with millions constructed over the years. Many of them face aging and are vulnerable to risks. Traditional bridge inspection methods are costly and time-consuming. They often rely on many manual laborers without providing system-level insights. Moreover, these outdated approaches make it difficult to obtain a clear representation of the current bridge health. This paper introduces a novel framework based on deep learning (DL) for identifying local bridge damage using acceleration data collected by Unmanned Aerial Vehicle (UAV)-mounted sensors. The framework employs WaveNet, which was designed as a generative audio DL model. Its causal dilated convolution deals with long-range temporal correlations without recurrence. Two WaveNet regressors are used to predict the damage location and its severity. The methodology is integrated with an optimized sensor spacing strategy for UAV deployments. The results demonstrate that the severity model achieved an average R2 = 0.98, while the location model reached R2 = 0.85. Optimal sensor spacing “S” was found at S = 1.0 m for localization and S = 0.5 m for severity. A field-simulated case was accurately identified by the two models, representing the potential of the proposed framework for more reliable bridge health monitoring.

As urban populations grow and climate-related risks intensify, the need for resilient, intelligent, and sustainable infrastructure has never been more critical. Traditional building systems often lack the real-time awareness and adaptability required to ensure safety, efficiency, and long-term sustainability. In this paper, we present the design and implementation of SmartWall - a sensory and compute-capable building construction component, developed using low-cost materials and equipped with multimodal IoT sensors to capture both environmental and structural data in real-time. These sensors monitor temperature, humidity, fire hazards, and movement through accelerometer and gyroscope readings. Sensor data is processed locally by a Raspberry Pi 5 unit and then wirelessly transmitted to a real-time dashboard for monitoring and intervention. This capability supports emergency decision-making and enhances situational awareness for public safety officials. To enhance power efficiency and integration, we introduce wireless charging via induction coils, eliminating the need for external wiring. Furthermore, we incorporate FRP (Fiber Reinforced Polymer) enclosures based on advanced 3D sandwich composite technology to encapsulate the sensing components. These enclosures offer superior protection against harsh environmental conditions such as fire, impact, and moisture, while allowing secure wire routing and sensor mounting for functionality and durability. We also address practical challenges such as power management, thermal buildup, and system resilience, proposing solutions that enhance the system's reliability. With SmartWall, we can build smart and affordable infrastructure systems to enhance sustainability and public safety in future smart cities.

This paper presents an improved theoretical damped single-axle vehicle-bridge dynamic interaction model to consider the effect of the contact patch size and motor-induced vehicle excitation. The contact patch issue is critical as it determines the minimum time step for simulation and maximum identifiable frequency, while the inclusion of the motor-induced vehicle excitation benefits the design of autonomous self-driven rather than towed vehicles. Estimations of the contact patch size for both the pneumatic tire and solid wheel scenarios are discussed. The contact patch responses, which degenerate into contact point responses when the contact patch size is assumed to be infinitely small, were derived for the first time both from the vehicle and bridge responses to confirm their equivalence. The minimum time step, which determines maximum identifiable frequency but is arbitrarily chosen in literature, is proposed to be determined by the vehicle speed and contact patch length. The procedures to extract multiple bridge triad information including natural frequencies, mode shapes, and damping ratios from the vehicle responses are also presented. Based on extensive parametric analyses, the sinusoidal vehicle excitation becomes more prominent as its amplitude and/or frequency increase and may overshadow the analysis of bridge frequencies of interest. The vehicle acceleration leads to a more accurate extraction of bridge mode shapes and damping ratios than the vehicle displacement since the displacement is dominated by the fundamental mode of bridge vibration. The damping ratio extraction shows an average error of 0.28% from the instantaneous amplitude of the vehicle acceleration signal.

The research in this paper focuses on detection and quantification of subsurface damage in reinforced concrete (RC) structures by the analysis of infrared images. Experimental investigations were performed on RC slabs with embedded reinforcing bars (rebars). The corrosion mechanism in rebars was accelerated using an electrical circuit setup. The initial temperature of the slabs was adjusted in the environmental chamber and IR images were taken at regular intervals while they were exchanging heat to laboratory air through convection. Records of IR images were post-processed using an objective thresholding method proposed based on unsupervised k-means clustering. The results were discussed in context and with respect to the ASTM D 4788-03 recommendation for minimum detectable thermal contrast (0.5 degrees C) from delamination in bridge concrete by infrared thermography (IRT). The conclusions lead to recommendations for improved practical implementation of IRT that contribute to the broader prospects of structural health monitoring (SHM).

The bridge weigh-in-motion (BWIM) technique uses the instrumented bridge on a large scale to identify the axle weight of a passing vehicle. Vehicle configurations, e.g., axle number and wheelbase, are crucial for the BWIM system, which require additional axle detectors. Free of axle (FAD) sensors are often used to obtain vehicle information, but they are only suitable for specific bridge types, such as slab-girder bridges. The concept of a virtual-axle-based algorithm, without requiring axle detectors, has been developed, and the validity of this algorithm has been verified numerically and experimentally. However, this algorithm assumes the vehicle speed as a known input, indicating that additional speed sensors/devices are still required in the BWIM system. Using this virtual-axle-based algorithm in a field test, it is found that the identification accuracy of the BWIM system is sensitive to the vehicle speed, and it shows poor recognition of vehicle configuration. To improve the recognition accuracy and remove vehicle speed detectors from the BWIM system, an extended BWIM system is proposed using the regularization technique and iterative approach. Both vehicular virtual axles and speeds are assumed in this approach. An error function based on the measured responses and theoretical ones is built to evaluate these assumed vehicle configurations and speeds. The effectiveness of the proposed approach is verified by the field tests. The results show that the proposed approach can obtain high recognition accuracy, which is close to Moses’s algorithm using FAD sensors. Compared with the previous virtual-axle-based algorithm, the recognition accuracy and robustness of the proposed approach are greatly improved. The proposed approach is still challenged by real-world traffic because this paper only considers the case when a single vehicle passes over the bridge. Nevertheless, the proposed extended BWIM system shows potential practical applications as it can further reduce costs and be applicable to more bridge types.

As large-span structures, reticulated shells are widely used in large-scale public building and act as emergency shelters in the event of sudden disasters. However, spatial reticulated shells are dynamic-sensitive structures; the effect of the initial structural damage on dynamic stability should be considered. In this study, a new nonlinear dynamic model of cylindrical reticulated shells with initial damage is proposed to investigate the effect of initial damage accurately. Firstly, the damage constitutive relations of the building steels are built based on the irreversible thermodynamic theory; furthermore, its fundamental equations are obtained using simulated shell methods. Then, the nonlinear vibration differential equations with damage are obtained and studied with support. Meanwhile, the nonlinear natural vibration frequency with initial damage is derivatized. After that, a bifurcation problem with initial damage is studied by using Flouquet Index, and the dynamic stability state at the equilibrium point is analyzed in depth. It is found that the local dynamic stability of the system is determined via its initial condition, geometric parameters, and initial damage. Moreover, the initial damage dominates over other influence factors due to its strong randomness and uncertainty for the same structure. The damage accumulation results in the transition of the equilibrium point. In addition, the nonlinear natural vibration frequency decreases to zero with the accumulation of the damage reaching 0.618; the local stability of cylindrical reticulated shells fails and they even lose whole stability. This study provides a theoretical foundation for the future investigation of whole stability with initial damage.

Recently, due to low cost and convenience, the concept of estimating the bridge's first natural frequency through indirect measurements of a passing vehicle has gained increasing attention-known as drive-by bridge structural health monitoring. As the vehicle acts as a moving mass added to the bridge, this system is nonstationary with time-varying characteristics. Most related studies assume constant bridge and vehicle frequencies of vehicle-bridge interaction (VBI), which is only appropriate when the VBI effect is negligible. When the vehicle mass is significant compared with the bridge mass, often a feature of railway bridges, the interaction effect cannot be ignored. Therefore, this paper presents a nonlinear time-frequency analysis approach to examine the time-varying nature of frequencies in the VBI system using the second-order synchrosqueezing transform. In comparison to the classical linear approaches, such as wavelet transform and short-time Fourier transform, the proposed method can significantly improve the energy concentration of the time-frequency representations, resulting in a clear pattern to show how the frequencies change. In addition, an indicator is proposed to automatically select the parameters with the proposed approach to obtain suitable results. Both numerical simulation and laboratory experiments are carried out to investigate the feasibility of the proposed approach. It is found that due to the time-varying nature of VBI, the frequencies of both vehicles and bridges are time-varying. Therefore, the extracted drive-by bridge frequency should be distinguished from that found using direct (on-bridge free vibration) measurements.

Composite structural insulated panels (CSIPs) are eco-friendly, high-performance materials, which not only good have mechanical properties, but also good waterproof, moisture-proof, fire-proof, and anti-corrosion characteristics, so they have been used to build envelope structures in recent years. However, how to improve stiffness of CSIPs remains unsolved. The poor stiffness is one of the biggest obstacles for the application of CSIPs in the load-bearing members of civil engineering. In this study, the layout of glass–polypropylene (PP) laminate layers is designed to enhance its stiffness, and this study applies CSIPs as load-bearing members of civil engineering for the first time. Thus, the bend model of CSIP thin-wall box-beams under uniform loading is built, based on Timochenko’s theory. The deflection curve equation is presented, considering shearing deformation. The expressions for the bending of normal strain flanges of the beam and the equation considering principal shearing strain at the beam’s web are obtained, respectively. Finally, mechanical properties of the thin-wall box-beam under uniformly distributed loads were performed by FE. FE results are entirely consistent with the theoretical results, thereby making the theoretical method applicable for the design of thin-wall box-beams, which are made of composite materials. Different from other beams, the shearing deformation is a critical factor that influences the deformation of thin-walled box-beams.

The development and application of new Fiber Reinforced Polymer (FRP) material and 3D printing construction technology provide a basis for making up for the shortcomings of traditional thin-shell structures and building new thin-shell structures with better performance. In this paper, a new 3D Printing Composite (3DPC) thin-shell structure is proposed, which is prepared using a FRP plate as a permanent base mold and combining it with 3D printing cement technology. Both the typical experiment and finite element numerical simulation analysis of the 3DPC thin-shell structure are carried out. The results show that the maximum load capacity of the 3DPC thin-shell structure is increased by 53.3% as compared with the corresponding traditional concrete thin-shell structure. The presence of the FRP sheet effectively delays the generation of initial cracks and enhances the ductility of components.

This paper presents a method of developing digital twins (DTs) of road bridges directly from field measurements taken under random traffic loading. In a physics-based approach, the full three-dimensional behavior of the bridge is represented using response functions and distribution factors. In contrast to conventional finite-element analysis, this approach focuses on the relationship between the applied loads and the measured responses, given the limitations on the information about the applied loads due to random passing traffic. At the same time, it takes advantage of some key features of bridge traffic loading that are consistent, regardless of the weights of the passing vehicles. The nature of traffic loading is that axles travel from one end of a bridge to the other and the response is a linear combination of axle weights and ordinates of the same influence line function, adjusted for relative axle locations. Small/medium span concrete slab-girder decks are the target structures of the study. The three-dimensional nature of such structures is a particular challenge, especially in the case of multiple vehicle presence. While these bridges are strongly orthotropic, there is a significant degree of load distribution between the girders immediately under the passing vehicle and girders under adjacent lanes. This is addressed using an iterative approach that uses transverse distribution factors. The proposed DT model is verified using both numerical simulation and field tests.

Infrared thermography (IRT) is a non-destructive technique capable of detection and localisation of hidden subsurface defects in components of transportation infrastructure, such as concrete bridges, thereby contributing to structural health monitoring (SHM). Addressing the lack of research on subsurface defect detection in concretes by convection heat exchange, and regarding the importance of laboratory studies for proper implementation of IRT, this paper presents results from recent laboratory investigations of IRT on concrete slabs with simulated hidden defects using a convective thermal excitation mechanism. The concrete slabs in this study had simulated defects ranging 5–25 mm in depth from the surface. These studies show the effect of initial temperature, heating/cooling process, temperature range and defect depth on thermal contrast in the concrete slabs. Furthermore, this paper compares the performance of the IRT as a non-contact sensor and thermocouples attached to the surface, in the evaluation of the thermal contrast on slabs with various defect depth. The dependence of maximum thermal contrast on the initial temperature and defect depth is explored using multivariate linear regression.