Civil Engineering Practice Research Articles

Civil engineering employers engagement in work-integrated learning

In this paper I provide two case studies of enhancing undergraduate students’ learning through Work-Integrated Learning (WIL). Both interventions involve university-industry partnerships with local and regional civil engineering employers. Industry Mentoring (2010-2024) is a curricular activity with an authentic assessment. Third-year students mentees (N=1130) were mentored by 298 practising civil engineers, representing 80 employers. Civil Engineering 4 Real (CE4R, 2012-2024) is a co-curricular hybrid programme of problem / project-based evening workshops (N=133), attended by 594 students with circa 2867 student attendances, supported by 218 engineers, representing 76 employers. Industry Mentoring and CE4R support students to network with employers, enhance their employability skills, and shape their professional identity. Through their pro bono assistance, employers have established a talent pipeline, recruiting students for summer placements and graduate jobs. For employers, their engagement provides evidence of enacting their Corporate Social Responsibility (CSR) through providing a social value contribution to a local university.

A comparative ensemble approach to bedload prediction using metaheuristic machine learning

An adequate bedload prediction is a challenging task in hydraulic engineering because of the complex sediment transport processes and corresponding environmental factors. The current study introduces a novel comparative ensemble approach using metaheuristic machine learning (ML) models to enhance the accuracy of bedload prediction using data from laboratory flume experiments. To uncover key insights in bedload transport prediction, several models are employed, such as K-Nearest Neighbours (KNN), Extra Trees Regressor (ETR), Linear Regression (LR), Random Forest (RF), Bagging Regressor (BR), and XGBoost (XGB). The coefficient of determination (R2) and root mean square error (RMSE) values vary between various models; however, XGB showed R2 = 0.99 and RMSE = 0.11. The sensitivity analysis emphasizes the crucial role of the Shields parameter in bedload prediction, while the SHAP analysis highlights the substantial influence of the XGB model in enhancing predictive accuracy. The REC curves show that BR, XGB, and RF, outperformed KNN and LR models. Furthermore, a graphical user interface has also been developed to facilitate user interaction with the predictive models, allowing for easier visualization, analysis, and interpretation of bedload transport predictions. Additionally, k-fold cross-validation was performed to assess the performance, consistency and robustness of the ML models. Thus, it can be concluded from the current study that the utilization of ML algorithms can improve accuracy, providing valuable insights for hydraulic engineers and highlighting the importance of ML models in civil engineering practices, particularly in bedload transport prediction.

Integrating AI and statistical methods for enhancing civil structural practices: current trends, practical issues, and future direction

The integration of artificial intelligence (AI) and statistical methods has revolutionized civil engineering by enhancing accuracy, efficiency, and reliability in various processes. This review systematically examines how advanced optimization techniques, including artificial neural networks (ANNs), Design of Experiments (DOE), and fuzzy logic (FL), are transforming civil engineering practices. It emphasizes the significant roles these methods play in addressing modern challenges such as structural health monitoring, damage detection, seismic design optimization, and concrete condition assessment. The review delves into case studies and real-world applications, showcasing the potential of these methods to create more resilient, sustainable, and cost-effective infrastructures. It critically examines the limitations and scalability of these techniques, identifying gaps in current research and practical challenges in real-world applications. The investigation also highlights the need for substantial computational resources, data privacy, security, and software interoperability. By addressing these issues, the review not only shows advancements in optimization techniques but also outlines future research directions, aiming to bridge the gap between theoretical developments and practical applications in civil engineering. This review serves as an essential resource for researchers, professionals, and policymakers interested in leveraging optimization techniques to advance civil engineering practices.

Mechanical performance of completely GFRP-tendon-reinforced concrete slabs considering compressive-tensile membrane action

Presently, increasingly more concrete structures strengthened with glass-fiber-reinforced polymer (GFRP) tendons have been employed in practical civil engineering owing to outstanding advantages, such as anti-corrosion, cost-effectiveness, and ease of production. These structures have demonstrated unique characteristics and mechanical response compared to steel-tendon-reinforced concrete structures in both service and ultimate load-bearing states. Therefore, in this study, to analyze completely GFRP-tendon-reinforced concrete slabs under localized wheel pressure, loading tests were conducted on two full-scale fabricated frame structures while considering the influence of membrane action on the cover slab. The mechanical response of these structures was revealed using the obtained load-displacement curves, ultimate bearing capacity, and crack morphology. The membrane action mechanism of the slab under weak lateral constraints was studied through the finite element method. The method to determine the effective membrane region of the reinforced slab was discussed and the corresponding ultimate bearing capacity was proposed considering the compressive-tensile membrane action under different failure modes. This model provides a preliminary reference for the quantitative determination of the structural properties of GFRP-tendon-reinforced concrete slabs.

Experimental investigations on the flexural behavior of compression-cast seawater sea-sand concrete beams reinforced with CFRP bars

To address the corrosion problems, environmental concerns, and achieve green and sustainable civil engineering practices, this study designed several compression-cast seawater sea-sand concrete (SSC) beams reinforced with corrosion-resistant carbon fiber-reinforced polymer (CFRP) bars (i.e., FRP-SSC beams) and their flexural behavior was experimentally investigated through four-point bending tests. Twelve FRP-SSC beams were designed, including three normal-cast and nine compression-cast ones. The critical parameters included the concrete casting forms, water-to-cement (w/c) ratios, and reinforcement ratios of CFRP bars (ρf). Effects of the concrete casting methods, w/c ratios, and ρf on the flexural responses of FRP-SSC beams were comprehensively studied. In addition, the flexural theorems were used to derive the prediction models of the equivalent stress block parameters (i.e., εcm, α1, and β1) and flexural strength (Mu) of compression-cast FRP-SSC beams. Moreover, based on the experimental results, effectiveness and accuracy of the proposed flexural strength design models were verified by using the existing design specifications. Finally, the results indicate that, (1) compression-cast FRP-SSC beams with the larger ρf (1.78 %) exhibited bending failure featured by compression concrete crushing, whereas that with the smaller ρf (0.75 %) shown splitting failure; (2) The cracking moments of compression-cast FRP-SSC beams increased significantly owing to the higher compressive strength and elastic modulus of compression-cast concrete (CCC); (3) Because of the pronounced brittleness of CCC, the improvement of the ultimate flexural capacity of compression-cast FRP-SSC beams was limited; (4) Under the same moment level, the maximum crack widths of compression-cast FRP-SSC beams were smaller than their normal-cast counterparts; and (5) the proposed flexural strength design models could yield more accurate, safer, and conservative predictions of flexural strength for both normal- and compression-cast FRP-SSC beams.

Calcium carbide residue-based material for the stabilization of dredged sludge and its use in road subgrade construction

Addressing the dual environmental challenges of dredged sludge disposal and the repurposing of industrial by-products, this study pioneers the application of Calcium Carbide Residue (CCR)-based materials for stabilizing dredged sludge in road subgrade construction. The proliferation of dredged sludge, characterized by high moisture content and pollutants, presents a significant environmental hazard, while CCR, a by-product of acetylene production, poses disposal and pollution challenges. This research endeavors to mitigate these issues by exploring the stabilization potential of CCR when mixed with dredged sludge, aiming to enhance the mechanical properties of materials used in road subgrade construction. Through comprehensive laboratory testing, including unconfined compressive strength assessments, scanning electron microscope analyses, and X-ray diffraction examinations, the study reveals the intricate physicochemical interactions between CCR and sludge. These interactions lead to notable improvements in material strength and moisture reduction, substantiating the efficacy of CCR-based binders in sludge stabilization. The CCR-based binder can make the strength of the stabilized sludge reach 215.4kPa and the water content drop to 32 %. Field tests conducted to assess real-world applicability confirm the laboratory results, demonstrating the treated material's suitability for road construction as per secondary road design criteria and its environmental safety. The research highlights the environmental and engineering benefits of using CCR-based materials for sludge stabilization, offering a sustainable solution to the challenges of dredged sludge management and industrial waste utilization. The findings represent a step forward in waste-to-resource conversion, promoting environmental sustainability in civil engineering practices.

Unsupervised quantitative structural damage identification method based on BiLSTM networks and probability distribution model

Current unsupervised structural damage identification methods mainly focus on unsupervised damage detection, i.e., detecting whether damage occurs, but rarely involve damage localization and quantification in practical civil engineering structures. Thus, based on bidirectional long short-term memory (BiLSTM) networks and a probability distribution model, i.e., generalized extreme value distribution (GEVD), this paper proposes an unsupervised structural damage detection, localization, and quantification method. The response correlations model among sensors under healthy conditions was built by employing the BiLSTM networks for reconstructing response data. Then, reconstruction error between reconstructed and true data is used as damage-sensitive features and thus is applied to detect and localized damage. In addition, a stable damage quantification result can be obtained based on the probability distribution model of damage-sensitive features. The proposed method is validated by using the data obtained from a numerical steel beam model and a practical long-span cable-stayed bridge. The research results demonstrate that the proposed method can effectively detect, localize, and quantify damage in an unsupervised manner, even with a limited number of sensors and low-order modal information in acceleration data. The proposed method possesses the capability to autonomously furnish alerts regarding structural damage location and severity under Environmental and Operational Conditions.

From Bin to Binder: Unleashing Waste Butter’s Potential as a Pioneering Bio-Modifier for Sustainable Asphalt Engineering

Exploring the interface of environmental sustainability and civil infrastructure development, this study introduces waste butter (WB), a byproduct of animal fat processing, as a novel bio-modifier in asphalt production. This approach not only recycles animal waste but also charts a course for sustainable infrastructural development, contributing to a reduced environmental impact and promoting circular economy practices. The experiments incorporated varying WB concentrations (e.g., 3%, 6%, and 9% by weight of binder) into standard AP-5 asphalt, employing advanced analytical tools for comprehensive characterization. These included thin-layer chromatography–flame ionization detection (TLC-FID), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Differential Scanning Calorimetry (DSC). The critical properties of the asphalt blends, such as penetration, softening point, viscosity, ductility, rutting factor (Dynamic Shear Rheometer), and thermal susceptibility (Penetration Index, Penetration–Viscosity Number), were assessed. FT-IR analysis indicated negligible chemical alteration with WB addition, suggesting predominantly physical interactions. TLC-FID showed a decrease in aromatic and asphaltene components but an increase in resin content, highlighting the influence of WB’s fatty acids on the asphalt’s chemical balance. The colloidal instability index (IC) confirmed enhanced stability due to WB’s high resin concentration. Meanwhile, SEM analysis revealed microstructural improvements with WB, enhancing binder compatibility. TGA demonstrated that even a minimal 3 wt. % WB addition significantly improved thermal stability, while the DSC results pointed to improved low-temperature performance, reducing brittleness in cold conditions. Rheologically, WB incorporation resulted in increased penetration and ductility, balanced by decreased viscosity and softening point, thereby demonstrating its multi-faceted utility. Thermal susceptibility tests emphasized WB’s effectiveness in cold environments, with further evaluation needed at higher temperatures. The DSR findings necessitate careful WB calibration to meet Superpave rutting standards. In conclusion, this research positions waste butter as a superior, environmentally aligned bio-additive for asphalt blends, contributing significantly to eco-friendly civil engineering practices by repurposing animal-derived waste.

Geopatial Technologies in Civil Engineering : A Critical Literature

This study investigates the role of geospatial technologies in enhancing the efficiency and effectiveness of civil engineering projects. Geospatial technologies, including Geographic Information Systems (GIS), remote sensing, and spatial analysis, have revolutionized the field of civil engineering by providing critical tools for planning, design, construction, and management of infrastructure projects. The integration of these technologies enables accurate terrain mapping, real-time monitoring, and data-driven decision-making, which are crucial for ensuring the safety, sustainability, and resilience of modern infrastructure. This research aims to explore the applications of geospatial technologies in various civil engineering projects, including highway planning, urban development, and environmental monitoring. The study also examines the potential benefits and limitations of these technologies and provides recommendations for their effective integration into civil engineering practices. By understanding the impact of geospatial technologies on civil engineering projects, this research aims to contribute to the development of more efficient, sustainable, and resilient infrastructure that supports the needs of modern society.

Sustainable utilization of incineration bottom ash in pavement construction: Environmental impacts and life cycle assessment

The global drive towards sustainable development has prompted a notable shift in civil engineering practices towards utilizing secondary materials, such as incineration bottom ash (IBA). The literature review underscores the relevance of waste incineration and the crucial need for effective IBA management strategies. Existing studies, however, reveal a dearth of studies within the life cycle assessment (LCA) framework focusing on understanding IBA characteristics and field conditions. The study addresses this gap by proposing an LCA to assess the environmental implications of substituting natural aggregates with IBA in pavement layers. A comparative LCA was carried out, where different scenarios of reuse of IBA (as produced, treated, or with restrictions) were assessed against a baseline scenario that represents the conventional use of aggregates in roads and landfilling of IBA. The ReCiPe technique, incorporating a hierarchist perspective, was employed to assess impacts across different problem-oriented categories. Additionally, this study also explores the impact of data variability on LCA outcomes, considering changes in landfill height, pavement infiltration rate, time duration, and IBA transportation distance. The analysis indicates that incorporating IBA in road layers can significantly reduce impacts on non-toxic categories like global warming by over 35 %. However, the leaching properties of untreated IBA contribute to a more than 28 % increase in human carcinogenic toxicity, rendering it environmentally unsuitable for road layer reuse. The comparative study illustrated that reusing treated IBA is the most sustainable approach, remaining effective up to a distance of d + 15 km. In cases where treatment is not feasible, utilizing IBA with restrictions is recommended up to a distance of d + 5 km (d represents the transportation distance for natural materials). This research fills a critical gap in the literature by providing a framework to assess IBA reuse in road construction, thereby advancing the discourse on sustainable materials management in the construction industry.

Numerical Modelling simulation technology of Nonlinear Multi-Field Coupled Ground Foundation

Contemporary civil engineering practices demand sophisticated computational tools to navigate the complexities of structural systems and their interactions with the ground. This paper introduces a novel numerical modelling simulation technology tailored for nonlinear multi-field coupled ground foundations. Ground foundations, pivotal interfaces between structures and underlying soil, undergo intricate interactions influenced by various physical phenomena, including soil-structure interaction, soil-structure-fluid interaction, and thermal effects. Traditional analytical methods often falter in capturing the nuanced nonlinear behavior of these coupled systems. Consequently, this paper proposes a comprehensive numerical modelling framework integrating advanced computational techniques to accurately simulate the complex interplay of multiple fields within ground foundations. The methodology incorporates finite element analysis, multi-physics coupling algorithms, and advanced constitutive models to capture the nonlinear behavior of soil, structure, and fluid interactions. Through detailed case studies and validation against experimental data, the efficacy and accuracy of the proposed simulation technology are demonstrated, emphasizing its potential to revolutionize the design and analysis of ground foundation systems in civil engineering applications. This paper represents a foundational contribution towards advancing the state-of-the-art in numerical modelling simulation technologies for nonlinear multi-field coupled ground foundations, fostering innovation and optimization in civil engineering practice.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Deflection and Elastic Modulus Assessment of Subgrade in Flexible Pavement mixed with Waste Tire Scrap Material

This study aims to assess the deflection and elastic modulus (Es) of subgrade in flexible pavements, focusing on a comparative analysis between pavements with clayey soil subgrade and subgrade modified with tire scrap. The research utilized Falling Weight Deflectometer (FWD) for measuring subgrade deflection, essential in evaluating pavement performance. The FWD applied a dynamic load to the pavement, with deflection measurements processed using the KGP-BACK software to calculate the Es of the pavement subgrade. This approach included assessing the Lower Layer Index (LLI) and Es of the subgrade. Findings revealed a notable reduction of 37.5% in deflection and 2.68 times increase in Es for the tire scrap modified subgrade pavement compared to the standard clayey soil subgrade pavement. These results demonstrate significant enhancements in pavement structure, underlining the potential of recycled materials in sustainable civil engineering practices.

Large-scale modular and uniformly thick origami-inspired adaptable and load-carrying structures

Existing Civil Engineering structures have limited capability to adapt their configurations for new functions, non-stationary environments, or future reuse. Although origami principles provide capabilities of dense packaging and reconfiguration, existing origami systems have not achieved deployable metre-scale structures that can support large loads. Here, we established modular and uniformly thick origami-inspired structures that can deploy into metre-scale structures, adapt into different shapes, and carry remarkably large loads. This work first derives general conditions for degree-N origami vertices to be flat foldable, developable, and uniformly thick, and uses these conditions to create the proposed origami-inspired structures. We then show that these origami-inspired structures can utilize high modularity for rapid repair and adaptability of shapes and functions; can harness multi-path folding motions to reconfigure between storage and structural states; and can exploit uniform thickness to carry large loads. We believe concepts of modular and uniformly thick origami-inspired structures will challenge traditional practice in Civil Engineering by enabling large-scale, adaptable, deployable, and load-carrying structures, and offer broader applications in aerospace systems, space habitats, robotics, and more.

The Practice of Forensic Structural Engineering in IABSE's Member Countries

The IABSE Task Group 5.1 on Forensic Structural Engineering aims to examine and to mitigate structural failures by sharing knowledge of technical, human and organizational causes of failure, in addition to methods and techniques in forensic investigation processes. Forensic engineering expertise has yet to be recognized worldwide as a specific domain of civil engineering practice, and this paper amends previously published technical reports in 2012, 2014 and 2015 by former IABSE Working Group 8 following a new survey carried out in 2020–2022. This new survey has been developed with the lessons learnt from the previous survey in 2013–2014, including the validation of the results obtained by eliminating potential subjectivity in the former assessment. This survey also includes further understanding of the institutional and national regulatory framework. The survey was completed by 22 IABSE member countries whose responses were provided by national experts on forensic structural engineering. This paper will allow the reader to familiarize him/herself with the state of the art in forensic structural engineering practice across 22 countries, allowing a national-level diagnosis. The survey will also help to identify the main challenges within each country related to forensic engineering. Furthermore, recommended steps, as suggested by the national experts, for the awareness and/or development of good industry practice within forensic structural engineering are highlighted in this paper. Online supplemental data for this article can be accessed at https://doi.org/10.1080/10168664.2024.2307411.

Mottainai in civil engineering – A message from Japan

This article examines the impact of Japan’s mottainai concept on civil engineering practices, focusing on the waste of materials, financial resources, and harmonisation of structures with the environment. The authors highlight the global challenges of environmental and social imbalances in the context of global warming. The Japanese concept of mottainai is presented as the key to shaping the civil engineering approach. The inefficient use of raw materials was analysed in terms of material waste. A transition to a closed-loop economy is recommended, emphasising the need for resource efficiency. In the context of financial resources, the need for efficient budget allocation and project management is emphasised. The section on harmonising structures with the environment focuses on aesthetics, environment, sustainable mobility, and an integrated approach to urban planning. Civil engineering projects should combine functional efficiency with respect to the environment, and the mottainai concept can be a tool for achieving sustainability in civil engineering. Innovative solutions, such as intelligent energy management systems, are recommended to improve construction efficiency.

Civil engineering and disaster resilience: A review of innovations in building safe and sustainable communities

Civil engineering plays a pivotal role in ensuring the safety and sustainability of communities, especially in the face of increasing natural and man-made disasters. This paper provides a comprehensive overview of innovations in civil engineering that contribute to disaster resilience and the creation of safe, sustainable communities. As the frequency and intensity of disasters continue to rise globally, adopting advanced engineering solutions that mitigate risks and enhance infrastructure resilience is imperative. This review explores cutting-edge technologies and civil engineering methodologies that address disaster-prone regions' challenges. It encompasses a range of innovations, from resilient structural designs and materials to advanced geotechnical engineering practices. The integration of smart technologies, such as sensors and monitoring systems, is discussed as a means to provide early warning systems and real-time data for disaster response. The paper also delves into sustainable practices within civil engineering, emphasizing the importance of eco-friendly construction materials and energy-efficient designs. The role of urban planning and land-use management in disaster risk reduction is highlighted, emphasizing the need for resilient infrastructure that can withstand diverse environmental threats. Furthermore, the review explores case studies and success stories from around the world, showcasing instances where innovative civil engineering practices have effectively minimized the impact of disasters and contributed to the creation of resilient communities. Lessons learned from these experiences offer valuable insights for future projects and policy development. This paper underscores the critical intersection of civil engineering and disaster resilience, providing a foundation for further research and development in creating safer, sustainable communities. By embracing these innovations, the field of civil engineering can contribute significantly to building a resilient future in the face of an increasingly uncertain world.

On the Current Situation of Safety Management in Civil Engineering Construction

Civil engineering information management is a mature and young subject, mature is civil engineering, civil engineering practice has been carried out for thousands of years, such as Qin Shi Huang built the Great Wall. The youngest is the information management of civil engineering, which ensures that the cost, quality, schedule and safety of the project are fully controlled through information management. Civil engineering information management is an emerging discipline that has only been proposed in recent years with the modernization of project management. Although some results have been received, there are still some problems. In order to solve these problems, it is necessary to clarify the significance of information management, discover problems in a timely manner, solve problems, and realize the informatization of construction project management as much as possible. Therefore, this paper discusses the practice of information construction of engineering project management.

Operator learning for urban water clarification hydrodynamics and particulate matter transport with physics-informed neural networks

Computational fluid dynamics (CFD) can be a powerful tool for higher-fidelity water infrastructure planning and design. Despite decades of development and demonstration over a wide range of water systems such as clarification basins, activated sludge processes, ozone contactors, etc., CFD remains primarily used in academic research, with limited application in civil and environmental engineering practice. This limitation is contributed by its higher computational cost and demand for specialized user skills. This, however, need not be the case, if a robust and efficient surrogate model can be developed from CFD simulations and independently deployed for engineering purposes. Leveraging the emerging scientific machine learning (ML) techniques of physics-informed ML and operator learning, this study develops a composite neural network (CPNN) for learning the flow hydrodynamics and particulate matter (PM) transport and fate in clarification systems. The CPNN consists of a deep operator network (DeepONet) as an encoder and a physics-informed neural network (PINN) as a decoder. In contrast to common “black box” and lumped ML approaches, the developed CPNN directly incorporates physics principles into its architecture. Furthermore, the CPNN is designed for process-resolved and operator learning, enabling it to predict spatial hydrodynamics and PM concentration distribution (i.e., contours) across different basin geometrics and loading conditions. Compared to CFD simulation, the developed CPNN model has significantly higher computational efficiency (∼ milliseconds) while demonstrating robust predictive capability. For predicting basin hydrodynamics across 10,000 test cases, the trained CPNN model achieves an R2 above 0.8 for 66.4% of cases and an R2 above 0.4 for 89.2% of cases. A similar performance is also demonstrated by the CPNN in predicting basin PM concentration. Further investigation reveals that basin geometrics that trigger bi-modal flow solutions can be particularly challenging for ML. Additionally, this study visualizes the dependency of basin hydrodynamics and PM concentration on basin geometrics and loading conditions, providing valuable insights for optimizing basin configuration. Lastly, the potentials and benefits of web-based applications, e.g., DeepXtorm, as a user-friendly interface for the developed CPNN model is discussed. This study represents the initial step toward achieving real-time higher-fidelity water infrastructure planning, design, optimization, and regulation.

Numerical Modeling of Two Adjacent Interacting URM Structures

Masonry structures in addition to their long heritage are still widely used in civil engineering practice. It should be emphasized that a lot of research has already been done on the seismic behavior of masonry structures. However, due to the nature of such a problem, its complexity and seriousness, the development of numerical models and their connection with experimental tests are always important. This is particularly significant considering their vulnerability to the action of horizontal forces generated during seismic excitations. In recent decades, many researchers have tried to capture the behavior of unreinforced masonry (URM) structures or reinforced concrete (RC) frames with masonry infills exposed to earthquakes, using different approaches. This paper tackles numerical modeling based on the finite element method (FEM) for the estimation of the dynamic response of two adjacent interacting URM units, subjected to shaking table motions. Geometrical and material properties of the specimen are provided by the Horizon 2020 project SERA-AIMS (The Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe – Seismic Testing of Adjacent Interacting Masonry Structures). The analyses of dynamic performance were executed in SAP2000 software. Obtained results on the numerical model provide useful guidelines for modeling the nonlinear seismic behavior of masonry buildings.