Engineering Challenges Research Articles

ENGINEERING CHALLENGES AND SOLUTIONS IN SMART GRID INTEGRATION WITH ELECTRIC VEHICLES

This paper explores the critical engineering challenges and innovative solutions for successfully integrating intelligent grids and electric vehicles (EVs), emphasizing the increasing need for a resilient and adaptive electrical grid as global EV adoption accelerates. It comprehensively examines the technological, infrastructural, and regulatory obstacles that must be addressed to ensure seamless integration, focusing on advanced energy management systems, grid stability amidst fluctuating demand, and incorporating renewable energy sources. The study delves into the infrastructural requirements, including the expansion of charging networks, upgrades to transmission and distribution systems, and the implementation of vehicle-to-grid (V2G) technologies, while also analyzing the necessary regulatory and policy frameworks, stressing the importance of clear standards, incentives, and public-private collaboration. The paper offers a forward-looking perspective on overcoming current challenges by reviewing recent advancements in innovative grid technology—such as high-capacity energy storage and artificial intelligence (AI) use for predictive maintenance and load balancing. It highlights the need for interdisciplinary collaboration among engineers, policymakers, and industry leaders to develop a cohesive strategy for future energy distribution while underscoring the role of AI in optimizing grid performance, predicting energy consumption patterns, and enhancing overall efficiency. Ultimately, the paper provides a comprehensive analysis of the current state of smart grid and EV integration, offering actionable insights for stakeholders and concluding with recommendations for future research and development priorities, with a strong emphasis on continued innovation and cooperation to achieve a sustainable and resilient energy future.

PMSOMA: optical microscope algorithm based on piecewise linear chaotic mapping and sparse adaptive exploration

The optical microscope algorithm (OMA) is a metaheuristic algorithm that draws inspiration from the magnifying functionality of optical microscopes. This study introduces an enhanced variant of OMA, termed PMSOMA, designed to mitigate the original version's limitations, notably its slow convergence rates and vulnerability to local optima. PMSOMA integrates a piecewise linear chaotic map to refine population initialization and augment diversity, alongside a sparse adaptive exploration mechanism to bolster search efficacy. The performance of PMSOMA was rigorously tested using a suite of 50 benchmark functions, the CEC2017 test suite, feature selection datasets, and three classical engineering challenges. The empirical findings confirm that PMSOMA surpasses both the original OMA and competing algorithms by delivering superior solutions, accelerating convergence, and demonstrating enhanced robustness in convergence.

Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving

The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

Preliminary design of a viaduct on new Highspeed line RS2 VRT Jižní Morava

AbstractThe newly proposed high‐speed rail in Czech Republic creates a set of new challenges for engineers in design overall. In this case, the most significant challenge was to design a viaduct over several obstacles due to the crossing of Special Area of Conservation (SAC), part of Natura 2000. This aims to limit impacts in short‐term, during construction and more importantly in the long term. Construction of the viaduct will mitigate an impact to the natural surrounding area, whilst allowing immigration of its natural habitats. Nevertheless, the viaduct design and operational speeds will reach 320 km/h with possible raising to 350 km/h limit, with minimal impact within the area.Initial length of the viaduct was over 1 300 m and the main requirement given by an infrastructure manager was to design a viaduct without railway expansion joints. Due to such a long distance, the viaduct had to be separated into several dilatation segments in order to avoid the use of rail expansion joint. The most susceptible parts of SAC were in proximity of the river Šatava and surrounding wetlands. This area created an obstacle, which had to be crossed by a long span avoiding the implementation of bridge piers. Poor geological conditions close to the watercourse had to be taken into consideration for design of pier foundation, therefore the span had to be shortened to a compromised length.The final design of the viaduct consists of 16 segments, of which 14 are continuous segments and 4 are single spans. Overall, there are 29 spans over the length of the viaduct. Construction method of incremental launching had been chosen to reduce the short‐term impact in the SAC.

Flow field characteristics and drag reduction performance of high–low velocity stripes on the biomimetic imbricated fish scale surfaces

AbstractImproving energy efficiency and cost reduction is a perennial challenge in engineering. Natural biological systems have evolved unique functional surfaces or special physiological functions over centuries to adapt to their complex environments. Among these biological wonders, fish, one of the oldest vertebrate groups, has garnered significant attention due to its exceptional fluid dynamics capabilities. Researchers are actively exploring the potential of fish skin's distinctive structural and material characteristics in reducing resistance. In this study, models of biomimetic imbricated fish scale are established, and the evolution characteristics of the flow field and drag reduction performance on these bionic surfaces are investigated. The results showed a close relationship between the high–low velocity stripes generated and the fluid motion by the imbricated fish scale surface. The stripes' prominence increases with the spacing of the adjacent scales and tilt angle of the fish scale, and the velocity amplitude of the stripes decreases as the exposed length of the imbricated fish scale surface increases. Moreover, the biomimetic imbricated fish scale surface can decrease the velocity gradient and thereby reduce the wall shear stress. The insights gained from the fish skin‐inspired imbricated fish surface provide valuable perspectives for an in‐depth analysis of fish hydrodynamics and offer fresh inspiration for drag reduction and antifouling strategies in engineering applications.

Fluid-acoustic monolithic simulation based on spectral element method to solve flows past a slotted circular cylinder at low Reynolds numbers

Aerodynamic noise resulting from the flow around cylinders is a significant engineering challenge in aviation and wind engineering. The phenomenon of alternating vortex shedding in the flow leads to vibration and noise generation. However, accurately describing both the flow field and the sound field is challenging due to the significant difference in magnitude between them. To tackle this issue, this work introduces the application of the spectral element method (SEM) and flow-acoustic monolithic simulation for solving the two-dimensional compressible Navier–Stokes equations at low Reynolds numbers. This study is to investigate the reduction of flow-induced noise through the implementation of slotting technology on a circular cylinder. This study focuses on examining two different slit width ratios, s/d = 0.15 and 0.25, with a slit angle of attack of 0°. A comparative analysis is conducted between a complete circular cylinder and a slotted circular cylinder. The findings indicate that the slotted cylinder exhibits reduced intensity of vortex shedding and an extended region of downstream vortex generation compared to the complete cylinder. Notably, when s/d = 0.25, the slotted cylinder demonstrates minimal noise generation. Even at s/d = 0.15, a significant reduction in flow-induced noise is observed. These results highlight the potential of utilizing slotting technology on cylinders to effectively mitigate aerodynamic noise. The application of SEM and flow-acoustic monolithic simulation shows their relevance in analyzing and designing noise mitigation techniques in aerodynamics. This work can develop innovative solutions to reduce noise and improve the performance of various applications in aviation and wind engineering.

Stability analysis of tunnel heading in clay with nonstationary random fields of undrained shear strength

The stability problem of a tunnel heading in clay remains a significant challenge in geotechnical engineering. Specifically, when considering the spatial variability of the soil, the stability factor may be influenced by geographically random fields. This study investigates the effect of random fields on a probabilistic analysis of a tunnel heading in undrained clay. The study assumes that the undrained shear strength of the clay increases linearly with depth due to a strength gradient factor. The random adaptive finite element limit analysis is employed to calculate the stability numbers for tunnel headings. Nonstationary random fields with varying vertical correlation lengths are simulated using Monte Carlo simulation technique. The stability analysis takes into account geometry parameters (i.e., cover depth ratio) and nonstationary random field of undrained shear strength parameters. (i.e., strength gradient, coefficient of variation, and vertical correlation length). The results of tunnel face stability using random adaptive finite element limit analysis have also been utilised to assess the probability of design failure over a practical range of deterministic factors of safety. In the context of probabilistic failure analysis, the failure mechanism resulting from varying vertical correlation lengths could influence the probability of design failure. The findings of this study can be of significant interest to tunnel engineering practitioners during the design phase of tunnel heading projects.

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Extensive computational fluid dynamics analysis of microchannel flow topology and friction factor in arrays of conical pin-fins

The design of integrated circuits presents an increasing challenge for engineers, who seek to identify effective methods for cooling the miniature electronic components that are becoming increasingly complex. One potential solution is the use of micro pin-fin heat sinks, which have the potential to be an effective thermal management technique. This study compares the potential thermo-hydraulic efficiency of micro heat exchangers with conical pin-fins, arranged in two alternative patterns. The flow topology was investigated using the critical points theory and Ω-criteria to gain a deeper understanding of vortical structures and flow separation. 75 variations of pin-fin arrays were simulated and analyzed. It is noteworthy that no pattern similar to bidirectional pin-fins has been studied previously. The input datasets for the simulations included pitch/height ratios ranging from 0.823 to 1.235, cone angles from 0° to 13.48°, and flow Reynolds numbers of 40–117. The numerical results show that Ω and kinetic energies can predict the onset of instabilities. The degree of conicity and the pattern affect the friction factor, typically reducing it. The conical shape and arrangement of pin-fins can also aid in stabilizing the flow. Furthermore, the dependence of the friction factor on pitch/height and Reynolds was quantified with the calculated mean relative error of 1.7%. Moreover, turbulence parameters and friction factors were used to evaluate the thermohydraulic properties, deliberately excluding heat transfer simulations. This approach allows a much wider range of geometric modifications to be investigated for the preliminary optimization of the thermal and hydraulic performance of microchannels.

Recent advances in conductive materials for printed electronics and printed technology

AbstractThe growing interest in flexible conductive materials and printed electronic components has spawned a series of new commercial applications, including smart devices, functional clothing, and intelligent packaging. This review summarizes the main preparation techniques for conductive ink fillers, while considering the influence of the source materials (metals, carbon nanoparticles and polymers) that affect the performance of the conductive inks. The preparation methods and related research advances in electronic components (flexible electrodes and sensors) based on inkjet printing and screen‐printing technology are summarized. In addition, the literature review reveals that the current research content rarely considers the influence of printing conditions on the quality of the printed electronic devices, thus indicating that numerous specific engineering challenges need to be overcome for the commercial application of printed electronics.

Recent Advances of the Ultimate Microbial Influenced Corrosion (MIC): A Review

Abstract: The persistent corrosion of metal surfaces poses a significant engineering challenge, including material degradation, loss of metal-structural integrity, and massive maintenance costs in various industrial, medical, and aerospace applications. Microorganisms such as archaea, bacteria, fungi, and microalgae can directly or indirectly influence metal corrosion. The degree of corrosiveness and corrosion healing varies depending on the microbe, medium, and metal substrate characteristics. Several attempts have been made to reveal answers to all questions about MIC. The published reports focused on testimonial failures and laboratory or field tests under varied situations. This review offers an overview of the most recent MIC research and emphasizes the scarcity of data on MIC detection, estimation, and the most recent approaches for MIC management. The review extends previously reported works and summarizes efforts for better understanding and cutting off MIC management in advanced technologies. Furthermore, it concludes with a final discussion of the current and future drawbacks and protective systems for preventing microbial-induced corrosion.

Software Engineering for IoT

This review paper synthesizes findings from nine re- search papers on software engineering for the Internet of Things (IoT). It assesses the current state of IoT software engineer- ing, addressing architectural design, development methodologies, communication protocols, security, and scalability. The review underscores the challenges and innovative solutions in the field, offering valuable insights for researchers and practitioners. It highlights the evolving landscape of IoT software engineering, emphasizing the need for continuous development to meet the growing demands of the IoT ecosystem. Thorough exploration of challenges in IoT software engineering, innovative solutions, emphasis on security and privacy, real-world case studies, scal- able architectures, interoperability, ethical considerations, and future trends are all integral components of this comprehensive examination of IoT software engineering. This review paper not only provides a snapshot of the current state but also offers a forward-looking perspective on the transformative potential of IoT and the role of software engineering in realizing it.

Lipid Nanoparticles for Brain Tumor Theranostics: Challenges and Status.

Lipid nanoparticles have been recognized as a powerful weapon for delivering various imaging and therapeutic agents to the localized solid tumors, especially brain tumors individually or in combination. Promisingly, lipid-based nanosystems have been considered as safe delivery systems which are even approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). One recent spotlight of lipid nanoparticles as COVID-19 mRNA vaccines where lipid nanoparticles play an important role in effectively protecting and delivering mRNA to the desired cells. As of now, successive progress in lipid-based nanocarriers, viz., nanoliposomes, solid lipid nanoparticles, ionizable lipid nanostructures, etc., with better biochemical and biophysical stabilities, has been noticed and reported. Moreover, lipid nanostructures have been considered as versatile therapeutics platforms for a variety of diseases due to their biocompatibility, ability to protect and deliver therapeutics to the localized site, and better reproducibility and reliability. However, lipid nanoparticles still face morphological and biochemical changes upon their in vivo administration. These changes alter the specific biological and pathological response of lipid nanoparticles during their personalized brain tumor theranostics. Second, lipid nanomedicine still faces major challenges of zero premature leakage of loaded cargo, long-term colloidal stability, and off targeting. Herein, various lipid-based nanomedicines for brain tumor imaging and therapeutics "theranostics" have been reviewed and summarized considering major aspects of preclinical and clinical studies. On the other hand, engineering and biological challenges of lipid theranostics systems with relevant advantages and guidelines for clinical practice for different brain tumors have also been discussed. This review provides in-depth knowledge of lipid nanoparticle-based theranostics agents for brain tumor imaging and therapeutics.

Innovative Bistable Composites for Aerospace and High-Stress Applications: Integrating Soft and Hard Materials in Experimental, Modeling, and Simulation Studies.

This study explores the development and performance of bistable materials, emphasizing their potential applications in aero-vehicles and high-stress environments. By integrating soft and hard materials within a composite structure, the research demonstrates the creation of bistable composites that exhibit remarkable flexibility and rigidity. Advanced simulations using COMSOL Multiphysics and 3D-printed prototypes reveal that these materials effectively absorb and dissipate stress, maintaining structural integrity under high-pressure conditions. Compression tests highlight the ability of bistable structures to bear significant loads, distributing stress efficiently across multiple layers. The innovative proposal of combining stiff and flexible materials within a single unit cell enhances bistable behavior, offering superior energy absorption and resilience. This work underscores the promise of bistable materials in advancing materials science, providing robust solutions for aerospace, automotive, and protective gear applications and paving the way for future research in optimizing bistable structures for diverse engineering challenges.

Artificial Neural Network-driven Optimization of Fe3O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation

Efficient degradation of industrial dyes remains a critical challenge in environmental engineering. This study introduces a novel Fe3O4 nanoparticles/PVDF macrospheres in a Fenton-like system, optimized using an Artificial Neural Network (ANN) for the degradation of Methylene Blue (MB). A feedforward backpropagation neural network model to optimize and predict the performance of this advanced oxidation process under various operational conditions. The model was trained, validated, and tested with robust datasets, demonstrating high predictive accuracy and generalization capability. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) during testing were 0.0200 and 0.1414, respectively, indicating precise predictions. The coefficient of determination (R²) and correlation coefficient (R) were exceptionally high at 0.9744 and 0.9871, affirming the model's ability to capture the underlying dynamics of the degradation process effectively. The ANN-driven approach not only enhanced the efficiency of the MB degradation process but also provided significant insights into the scalability and applicability of the Fe3O4/PVDF system for practical water treatment solutions. This study underscores the potential of integrating advanced machine learning techniques with chemical engineering processes to achieve sustainable and efficient environmental management solutions, particularly for the treatment of recalcitrant wastewater contaminants.

Large Fiber Array Spectroscopic Telescope: Optical Design for a Scalable Unit Telescope

The Large Fiber Array Spectroscopic Telescope (LFAST) concept involves the utilization of 2640 individual, 0.76 m “unit telescopes” to collect light from a target object. Through optical fibers, the lights from all telescope foci are directed to a high-resolution spectrograph, with fibers subtending a 1.47 arcsec diameter on the sky. The total light-collecting area of all telescopes combined is 1200 m2. Given the telescope’s application for spectroscopy of seeing-limited images, the need for coherence is eliminated. The primary goal of the LFAST prototype is to demonstrate a telescope design that can be rapidly and cost-effectively replicated to achieve a 1200 m2 collecting area and match the capabilities of extremely large telescopes (ELTs) presently under construction. Engineering, manufacturing, and design challenges unique to LFAST differ significantly from those encountered in traditional ELTs. Considering that the unit telescope is to be replicated thousands of times, reducing the manufacturing cost per telescope is essential. The aim of the described design is to achieve this goal, with strong considerations for manufacturability, alignment, few moving parts, and high yield. Experience from the first prototype unit telescope built in 2022 will be leveraged to build the first 20-unit tracking module in 2024, with a collecting area of a 3.4-m-diameter mirror. The design is minimalistic and features commercial tolerances, thereby ensuring cost-effectiveness in manufacturing, assembly, and alignment processes. Moreover, the telescope is designed to operate at angles as low as 70° from the zenith by laterally translating a single lens to correct for atmospheric dispersion. We present a combined average of over 80% encircled energy across 2640 Monte Carlo trials. Simulations were conducted using commercially specified tolerances and capabilities to model the overall performance of the array.

Optimal scheduling of multiple entities in virtual power plant based on the master-slave game

As the energy market evolves into a dynamic and interactive landscape, the distributed nature of virtual power plant (VPP) becomes increasingly significant. This shift highlights the limitations of traditional centralized optimization approaches in capturing the complex interplay among various stakeholders. In response, this article introduces a novel multi-entity distributed collaborative optimization strategy for an integrated energy system (IES) that incorporates a VPP, electric vehicles, and carbon capture technologies, under the Stackelberg master-slave game framework. Within this framework, the VPP operator (VPPO) assumes the role of the leader, while the energy supply aggregator (ESA), customer side residential load aggregator (CSRLA), electric vehicles aggregator (EVA), and carbon treatment system aggregator (CTSA) are positioned as the followers. The paper delves into the development of interaction strategies for each entity, aiming at achieving optimal objectives. The study undergoes by presenting the mathematical model of the VPP-integrated energy system, which is seamlessly integrated into the master-slave game structure. This integration facilitates the creation of a distributed multi-entity cooperative optimization model featuring a single leader and multiple followers, with the uniqueness of the Stackelberg equilibrium being rigorously established. Therefore, the research employs a hybrid nutcracker optimization algorithm and quadratic programming (NOA-QP) method to address the engineering challenge of optimizing operator energy pricing. The case study conducted demonstrates that the proposed scheduling methodology improves the VPP system profit by 10.80%, increases the aggregation profit by up to 1317.22%, optimizes the carbon emissions by 15.59%, and significantly improves the solution efficiency of the VPP by 99.29%.

Treatment process of pre-coagulated waters involving polyethylene (PE) microplastics by ultrafiltration membranes coupled without or with pre-deposited aggregate-based layer

In recent years, environmental and ecological issues caused by microplastics pollution have received widespread attention. The coexistence of polyethylene (PE) microplastics with other soluble and particulate contaminants in water environments is considered to pose a new challenge for water treatment engineering. This study first focused on the combined effects of microplastic size (13 and 150 μm) and concentration (0, 20 and 100 mg/L) of floc properties in the coagulation-flocculation process (CF). The results showed that the involvement of PE microplastics hindered larger floc formation but enhanced the floc strength. The presence of either smaller microplastic particles at a fixed concentration (100 mg/L), or a higher concentration of 13 μm microplastic particles resulted in smaller and more highly-branched floc aggregates. During ultrafiltration of pre-coagulated water (CF-UF), the “13 μm + 100 mg/L” case exhibited less severe membrane fouling than the other cases (“without PE”, “13 μm + 20 mg/L” and “150 μm + 100 mg/L”). Comparing the CF-UF process in the “without PE” and “13 μm + 100 mg/L” cases, the pre-deposited aggregate-based layer could give a lower degree of membrane flux decline and mitigate membrane fouling during ultrafiltration of pre-coagulated water (CF-UFPL). Moreover, both the involvement of PE microplastics and the properties (thickness and compactness) of the pre-deposited layer affected membrane filterability and fouling degree. Lastly, the influencing mechanisms of coexisting microplastics in UF, CF-UF and CF-UFPL processes were explored by comparatively analyzing the correlation of pre-coagulated floc physicochemical characteristics with membrane filterability, water purification efficiency and membrane fouling degree in both the absence and presence of microplastic particles.

Thermostability and activity improvement in l-threonine aldolase through targeted mutations in V-shaped subunit

l-threonine aldolase (LTA) catalyzes the synthesis of β-hydroxy-α-amino acids, which are important chiral intermediates widely used in the fields of pharmaceuticals and pesticides. However, the limited thermostability of LTA hinders its industrial application. Furthermore, the trade-off between thermostability and activity presents a challenge in the thermostability engineering of this enzyme. This study proposes a strategy to regulate the rigidity of LTA's V-shaped subunit by modifying its opening and hinge regions, distant from the active center, aiming to mitigate the trade-off. With LTA from Bacillus nealsonii as targeted enzyme, a total of 25 residues in these two regions were investigated by directed evolution. Finally, mutant G85A/M207L/A12C was obtained, showing significantly enhanced thermostability with a 20 °C increase in T5060 to 66 °C, and specific activity elevated by 34 % at the optimum temperature. Molecular dynamics simulations showed that the newly formed hydrophobicity and hydrogen bonds improved the thermostability and boosted proton transfer efficiency. This work enhances the thermostability of LTA while preventing the loss of activity. It opens new avenues for the thermostability engineering of other industrially relevant enzymes with active center located at the interface of subunits or domains.

Construction engineering to transform the composite frame of YY London

30 South Colonnade was a 13-storey composite framed office building, completed in the early 1990s as part of the first phase of London’s Canary Wharf redevelopment. Between 2020 and 2023 it was refurbished to become YY London, a highly sustainable modern workspace. A key aspect of the project has been retention and reuse of much of the existing structure, dramatically reducing the new development’s embodied carbon impact. This paper describes the way the composite steel-framed building’s original structural configuration was developed to accommodate the move to on-screen share trading in the 1980s. It then outlines the reasons that change why change was needed for the 2020s. A key feature of the construction engineering was demolition that disconnected several bays of the original bracing from the surrounding slab, requiring staged construction of temporary stability, and complicated by an unusual decision by the original designers.UNSDG 12 Responsible Consumption and Production is stimulating architect, engineers, and clients to propose more extensive reuse of structures and their embodied carbon. This paper describes the construction engineering challenges that these schemes generate for contracting teams, and some of the solutions that can be implemented to preserve and reuse the embodied carbon of composite buildings.