• Systematic review of various applications of SASW in civil engineering. • Detailed analysis of 37 studies sourced from Web of Science and Scopus. • Five thematic groups of SASW utilization across civil engineering domains. • Discussed methodological adaptations within the SASW applications. • Challenges and opportunities for the future development of the technique. The spectral analysis of surface waves (SASW) method is a geophysical seismic testing technique, widely adopted in civil engineering. This systematic review examines the broad applications of SASW by analyzing various studies sourced from Web of Science and Scopus. The identification, screening, and assessment of the sourced articles, following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement yielded 37 studies for the analysis. The SASW applications are categorized into five thematic groups: geotechnical site characterization, foundation and structural analysis, seismic analysis and microzonation, pavement and railway infrastructure, and specialized applications. These applications demonstrate the adaptability and effectiveness of SASW in various civil engineering scenarios, which are crucial for sustainable infrastructure development and maintenance. This review provides a comprehensive overview of SASW's contributions and methodologies, aiming to help practitioners and researchers optimize geophysical methods for engineering challenges that are aligned with ongoing advancements in the field.

The ConcreteCARB dataset provides a comprehensive repository of 903 high-resolution images of concrete surfaces evaluated using the phenolphthalein test for carbonation detection. This data was collected under controlled laboratory conditions and aims to support artificial intelligence applications in civil engineering, especially in structural health monitoring tasks. The images are systematically organized into two distinct classes: "Carbonated Samples" and "No Carbonation Presence," enabling binary classification approaches. All samples were manually tested, split, and visually labelled by expert engineers to ensure reliable ground-truth classification, in accordance with standardized procedures. The dataset includes images of concrete prism elements fabricated with varying mix designs, incorporating different water-cement ratios and additives, such as industrial silica waste and natural admixtures derived from Opuntia ficus-indica. The specimens were subjected to natural atmospheric carbonation conditions for 180 days, and their carbonation fronts were revealed by phenolphthalein staining. The samples were then split manually with a chisel and hammer, and photographic documentation was performed with a Samsung SM-S901U1 smartphone using predefined settings to ensure consistency and quality across the dataset. ConcreteCARB is intended for researchers, engineers, and data scientists working on machine learning, deep learning, and computer vision solutions for concrete diagnostics. It provides valuable training and benchmarking data for the development of automated detection, classification, and segmentation models for carbonation damage assessment. Furthermore, the dataset can serve as a foundational tool for cross-comparative studies on the efficacy of AI techniques in materials degradation analysis. The openly accessible nature of the dataset through a public repository supports reproducibility and encourages the extension of AI applications in concrete durability and sustainability studies.

The aim of this study is to determine activitiy concentrations of the naturally occurring radionuclides (226Ra, 232Th, 40K) in granitic rocks from northeast of Turkey and to evaluate their suitability as building materials based on their radiation hazard indices. Activity measurements were interpreted together with geochemical properties and assessed through a multidisciplinary approach involving physics, geology, civil engineering, and multivariate statistics. The mean activity of 226Ra, 232Th, and 40K were found to be 30.10, 52.59, and 805.03 Bq/kg−1, respectively; which are higher than 32 Bq/kg−1 for 226Ra; lower than 45 Bq/kg−1 for 232Th, and higher than 420 Bq/kg−1 for 40K global mean values, as reported by the United Nations Scientific Committee on the Effects of Atomic Radiation. Radiological hazard parameters such as absorbed dose rate (D), annual effective dose equivalent indoor (AEDEindoor), annual effective dose equivalent outdoor (AEDEoutdoor), activity utilization index (AUI), and excess lifetime cancer risk (ELCR) yielded average values of 79.24 nGy/h−1, 389.00 µSv/yr−1, 97.25 µSv/y−1, 167.30 Bq/kg−1, and 0.98 and 340.38 µSv/y−1 for the Duzkoy samples, respectively. Numerical results indicate that these parameters exceed global mean values. The findings suggest that Düzköy granites are more appropriate for exterior applications; however, due to their relatively high 232Th-40K concentrations, they should be carefully assessed before use in interior environments. Further radiological health investigations are recommended for the region.

Acoustic emission (AE), as a highly sensitive structural health monitoring technology, has been widely applied to the damage detection of engineering structures. This paper systematically reviews the research progress of AE-based damage detection methods in civil engineering based on the literature mostly in the past 10 years. This review focuses on three types of AE-based damage detection methods for the engineering structures made of three commonly seen materials and having four different damage problems. Specifically, the detection methods are classified into three categories, including AE feature-based methods, waveform-based methods, and intelligent methods using AE physical models or data-driven models. They are further divided into nine subcategories of approaches for detailed analyses. The involved engineering structures are classified according to different materials and different damage problems for providing a cross-understanding of “methods—materials types—damage types” relationship. It is very challenging for AE-based monitoring technology to realize quantitative assessment, although proven to be an effective way for detecting damage existence. Therefore, the quantitative assessment approaches among the AE-based detection methods are explored in detail, which take advantage of explicit features or implicit relationships for grading the stages of damage development and quantifying damage severities. In responding to the limitations undermining the application of AE in civil engineering, possible solutions and future trends are provided and discussed. Through in-depth analyses of the methodological innovation and applications of the AE-based methods in damage initiation identification, damage expansion tracking, and damage quantification, this study potentially provides support for appropriate method selection and technical breakthrough of the challenges, thereby boosting the applicability of AE technology to engineering structures and reducing the engineering safety risks caused by damage development.

Soilbags represent an emerging three-dimensional geosynthetic reinforcement technique, valued in permanent civil engineering for their structural strength, site adaptability, and economic efficiency. Despite growing application, the fundamental mechanisms of enhancing the deformation modulus of reinforced soil have not been fully elucidated. This study advances the understanding by integrating field, theoretical, and experimental approaches to unravel the modulus‑enhancement mechanisms. Field plate load tests on single‑layer soilbag‑reinforced foundation based on soil‑rock mixtures recorded an average increase in deformation modulus of approximately 23.4% compared to unreinforced soil-rock mixtures, confirming the technique's practical performance. A unified stress-strain framework is developed, which explicitly incorporates the additional confinement stress generated by geotextile tension and traces the resulting transition in stress paths of the encapsulated soil, thereby offering a mechanistic interpretation of modulus improvement. The framework is validated through unconfined compression tests on both clay‑ filled and sand‑filled soilbags, which further clarify how tensile confinement actively redistributes internal stress paths. The results reveal that the enhanced modulus arises from the coupled interaction between compressive hardening of the infilled soil and tensile confinement provided by the geotextile bag, offering a mechanistic basis for optimized design and application of soilbag reinforcement.

Accurate estimation of structural damping is essential for seismic performance assessment and design for earthquake-resistant buildings. From a sustainability perspective, reliable evaluation of dynamic properties is crucial in extending the service life of existing structures and reducing the need for material-intensive interventions. Ambient vibration measurements enable non-invasive identification of damping characteristics, supporting sustainable assessment of the built environment. This paper presents an analysis of the dynamic response of a four-story reinforced concrete structure. Ambient vibration recordings are obtained with Geodas Aquisition Station and one-second velocity sensors made by Butan Service And Tokio Soil Ltd., available from CERS (Seismic Risk Assessment Research Center) research center from TUCEB (Technical University of Civil Engineering of Bucharest). The sensors were installed at the top level of the analyzed structure. The method used for estimating the damping ratio is the Random Decrement Technique (RDT). The influence of the several parameters involved in the method is investigated, such as the triggering value, the dimension of the time window sub-samples, and the number of cycles considered within a window relative to the natural period of the structure. For the analysis of the parameters specific to the RDT method, computational routines were developed using syntax compatible with OCTAVE/MATLAB R2019b. Filters were applied to isolate the natural vibration modes. The variability in the parameters demonstrates that the developed method is robust.

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals.

Abstract - Surveying is one of the most important activities in civil engineering, as it provides the basic data required for planning, design, and construction of infrastructure projects. However, traditional survey data processing methods involve manual conversion, drafting, and analysis using CAD software, which is time-consuming and sometimes prone to human error. To overcome these limitations, this project presents an AI-Based Surveying System for Leveling and Contouring. Overall, this project demonstrates how Artificial Intelligence can modernize civil engineering surveying by making it faster, smarter, and more efficient. Key Words: Artificial Intelligence, Surveying Automation, Contour Generation, Leveling, Longitudinal Section, Cross Section, KML to CSV Conversion, Terrain Modeling, Civil Engineering, AI Chatbot System.

Climate change increasingly necessitates infrastructure systems that are not only resilient but also socially responsive. Traditional data collection measures hardly capture real-time local experience, and therefore, there are planning gaps regarding climate-related risks. This study aims to explore how social media can be integrated into climate-resilient infrastructure planning, particularly by filling the gap between civil engineering practice and people's digital participation. Using a qualitative descriptive approach, data were collected from two coastal cities in Southeast Asia through semi-structured interviews among engineers and citizens, online observation, and social media monitoring. Thematic analysis was conducted using NVivo software, and inter-coder reliability was assessed by double-coding 20% of the transcripts, achieving 90% agreement. There were four major themes found in the research: (1) social media as an environmental risk-sensor for real-time sensing; (2) impact of public sentiment on technical design decisions; (3) issues verifying and incorporating crowdsourced data; and (4) adaptive redesign recommendations based on digital crowd responses. Citizen reports geo-tagged were shown to correspond to infrastructure vulnerabilities such as clogged drainage and low-lying ground. In short, social media offers a powerful tool for democratizing infrastructure planning. New frameworks will have to integrate crowdsourced intelligence into digital platforms, such as urban digital twins and real-time dashboards, to enhance climate resilience and spatial equity.

The field of microbially induced calcium carbonate precipitation in civil engineering has seen significant advancements, with the emergence of novel approaches such as bacterially induced calcium carbonate precipitation and the exploration of fungal induced calcium precipitation (FICP). FICP, in particular, has garnered attention due to its unique characteristics, including the formation of 3D reticular hyphae by filamentous fungi, which offer increased nucleation sites for applications such as crack repair and soil stabilization. Further, advancements in fungal-mediated microbial cementation, self-healing concrete, and heavy metal immobilization techniques have transitioned from theoretical concepts to practical applications. In this review, we summarize the current mainstream mechanisms of FICP and discuss its potential advantages and disadvantages. By reviewing recent research progress on FICP in civil engineering, we propose new directions for future research and applications in this field.

ABSTRACT In the field of civil engineering, the analysis of the composition of alkali‐sprayed concrete is of great significance for material research. However, there are currently no existing machine learning (ML) methods suitable for analyzing the composition of alkali‐sprayed concrete. Therefore, we developed an ensemble learning (EL) algorithm combining a multilayer perceptron (MLP) and a random forest (RF) to infer the composition of crushed test blocks. The results show that this EL method improves the training algorithm's ability by 30% compared to single‐stage ML algorithms and reduces the prediction error by 70% compared to traditional ML algorithms. This proves the feasibility of the EL algorithm in inferring the composition of alkali‐activated concrete (AAC) materials. This provides a feasible method for using ML to analyze the composition of AAC.

Abstract This paper establishes an enhanced analytical framework for capturing the nonlinear dynamic behavior and phase‐plane evolution of functionally graded carbon‐nanotube‐reinforced magneto–electro–elastic (FG‐CNT/MEE) sandwich panels operating under simultaneous mechanical, thermal, electrical, magnetic, and hygro–mechanical actions. The proposed multifunctional panel consists of two MEE face sheets and a CNT‐reinforced nanocomposite core, in which the nanotube content is distributed according to several functional gradation patterns (FG‐O, FG‐V, and FG‐X) to tailor stiffness and electromechanical coupling. The governing equations are rigorously derived using Reddy's higher‐order shear deformation theory (HSDT) in conjunction with Hamilton's principle, incorporating the synergistic influences of temperature rise, moisture diffusion, electromagnetic potentials, geometric imperfections, and Pasternak‐type foundation parameters. A combined Bubnov–Galerkin reduction and fourth‐order Runge–Kutta time‐integration scheme is employed to efficiently track transient nonlinear responses. The parametric study reveals several important physical trends. Hygrothermal environments strongly soften the structure, causing pronounced frequency reductions and amplified nonlinear deflections, while elastic foundations exert a dominant stiffening effect that effectively suppresses vibration amplitudes. Notably, the FG‐X CNT distribution consistently outperforms other patterns in terms of vibrational stability due to its optimized stiffness gradient, and increasing thermal loads drive phase‐plane trajectories toward weakly stable, high‐energy oscillatory regimes. The proposed framework offers a robust and computationally efficient tool for predicting, assessing, and optimizing the nonlinear dynamic performance of advanced CNT/MEE sandwich structures. The findings provide important design guidelines for aerospace, defense, and civil engineering components intended to function in harsh thermo–electro–magneto–mechanical environments.

<p><strong>Background:</strong> Fraudulent financial statements represent the most financially damaging form of fraud and the construction sector ranks as the highest risk sector globally for this scheme. Alongside, Indonesia currently ranks as the third nation for fraud cases across Asia Pacific, creating an urgent need to examine this phenomenon within its national context. <p><strong>Objective:</strong> This study aims to investigate the drivers of fraudulent financial statements through financial shenanigans within Indonesian heavy construction and civil engineering firms using the fraud heptagon framework. <p><strong>Research Methods:</strong> This study employs a quantitative panel data analysis method using secondary financial data from 13 publicly listed companies (2017-2023), selected with purposive sampling, and analyzed with fixed effect regression models. <p><strong>Research Results:</strong> he findings reveal that intense financial target pressures and high employee turnover significantly elevate fraud risk. In contrast, external pressures from financial leverage demonstrates a counterintuitive mitigating effect. Furthermore, most conventional fraud triggers show no statistically significant influence. <p><strong>Originality/Novelty of Research :</strong> This research contributes to the literature by integrating financial shenanigans analysis within the Fraud Heptagon framework, especially allowing the use of the religiosity variable while still considering the integrity variable.

This study presents an integrated framework for seismic performance assessment and multi-objective optimization of a G+12 reinforced concrete (RC) high-rise residential building using STAAD.pro and the NSGA-III algorithm. This research contributes a reproducible, automation-based framework for sustainable and code-compliant seismic design, facilitating performance-driven decisions for civil engineers, planners, and stakeholders. Future work may extend toward lifecycle cost modeling, nonlinear time-history analysis, and soil-structure interaction.

The growing amount of waste materials, such as plastics, rubber, construction debris, and industrial by-products, presents serious environmental challenges because they do not break down easily and are hard to dispose of. Recycling these materials for road construction provides a sustainable and eco-friendly solution. It reduces pressure on landfills and conserves natural resources. By incorporating recycled materials like waste plastic, crumb rubber from old tires, reclaimed asphalt pavement (RAP), and fly ash into road construction, we can address waste management issues while improving pavement performance. These materials can enhance properties such as durability, deformation resistance, and water repellency, while possibly lowering construction costs. This process supports the idea of a circular economy, where waste becomes valuable resources and reduces the environmental impact of infrastructure development. Research and case studies have shown that roads made with recycled materials can equal or exceed the performance of traditional roads when designed and built properly. This method supports global goals for sustainable development and lowering carbon footprints, making it a promising option for future infrastructure projects. Overall, using recycled materials in road construction is a practical, cost-effective, and environmentally friendly innovation in modern civil engineering.

Recently, geopolymers, a type of inorganic non-metallic cementitious materials, have attracted considerable attention as an alternative to ordinary Portland cement (OPC) and as an effective pathway to mitigate energy consumption and minimize CO2 emissions. The paper proposes a method of geopolymer design to achieve best mechanical properties of the developed material from the civil engineering perspective. Using a ternary plot, the authors selected specific proportions of geopolymer ingredients which predetermine such properties as high workability, high compressive and flexural strength. In the first stage of the research, a mixture of sand, fly ash, and alkaline activators were used to initiate the polymerization process which allowed to form specimens designated for further tests. The promising properties of geopolymers and the lack of access to OPC on the Moon have led to the consideration of using geopolymers as a building material in the construction of future extraterrestrial bases. In the second stage, simulant of the Moon regolith was utilized. The achieved results justify the claim that the proposed formulation method of geopolymers, designed for civil engineering applications, is useful both for terrestrial and extraterrestrial applications.

In civil engineering, information systems are increasingly being utilized, particularly Building Information Modeling (BIM) technology. BIM is currently most prominently used in the design and construction phases, with less intensity observed in the implementation of solutions in later phases of operation and/or demolition. However, these issues mainly concern recently constructed objects, for which a digital twin was created at the design stage, greatly facilitating the decision-making process for managers to implement such solutions. In this article, the authors focus on presenting an example of using the Common Data Environment (CDE) platform for managing an existing building, for which 3D documentation was not created in earlier stages of the lifecycle. For analysis and as an attempt to implement the use of BIM technology, building D-2 located on the AGH campus was selected. Virtual documentation in the form of a “digital twin” was prepared for the selected object. The traditional and currently practiced property management plan was analyzed. Firstly, a plan for repetitive tasks was presented, including required building inspections and cyclical work performed. Subsequently, a process of action in case of a selected failure was developed. The traditional management plan was compared with the one prepared using the digital platform. The advantages and disadvantages of each solution were identified, and the validity of introducing process improvement for building administration using the selected tool was verified.

The construction industry is increasingly exploring alternatives to natural aggregates, driven by sustainability concerns and landfill waste reduction. Blast furnace slag, a byproduct of steel manufacturing, exemplifies this shift, serving as a substitute aggregate or concrete additive. This transition supports the circular economy principle, where yesterday’s waste transforms into today’s resources. Key to this practice is the precise determination of material parameters, which vary depending on their origin. Among these, the filtration coefficient is critical, affecting the performance of anthropogenic aggregates in construction and infrastructure. It indicates how well materials transmit water, a factor vital for structural integrity. Machine Learning (ML) presents a promising tool for estimating such parameters efficiently. This paper explores various ML techniques for predicting the filtration coefficient, comparing their effectiveness and examining the impact of the physical properties of aggregates on model accuracy. Through this approach, the paper aims to identify the most suitable methods for parameter estimation, which could enhance the durability and stability of constructions that utilize recycled materials. This research not only contributes to the field of civil engineering but also advances sustainable practices within the industry.

There have been several major natural disasters that have attacked Kupang City in the last five years, namely the Seroja tropical cyclone disaster, a 5.1 Magnitude earthquake, and a moving mountain disaster. With several disasters that occur quite often in NTT Province, especially Kupang City, it is undeniable that this has been predicted and warned by BMKG to the Community. This proves that the Community can be said to be unprepared to face and mitigate natural disasters that will occur. Therefore, based on this background, a Community Service activity was held in the form of Natural Disaster Mitigation Training and Simulation in one of the locations in Kupang City, namely the Nunsui, Oesapa Village. The training materials that will be provided are in the form of natural disaster mitigation (before, during, and after it occurs) from three examples of civil engineering fields, namely construction, transportation, and cost management. It is hoped that with the training and simulation activities, the community can understand how dangerous the disaster that might hit the inhabited area is, and the community can respond better to the intended disaster mitigation methods

The selection of sensor systems for structural health monitoring (SHM) often relies on empirical practices, lacking methods to optimize multi-sensor combinations for both technical and economic performance. This study proposes a novel Function–Structure–Economy (FSE) framework, developed using design science principles. The framework was validated through a case study of a three-story frame-shear wall structure, employing quantitative analysis of functional coverage and cost-effectiveness. Four sensor combinations were evaluated by quantifying coverage of key parameters (displacement, settlement, vibration, strain, cracks). For the economic dimension, the trimmed mean method was applied to sensor market data to exclude outliers and establish robust cost inputs. Results identify an optimal suite (MEMS accelerometers + FBG sensors + Tilt sensors), achieving 80% functional coverage at a 25% lower unit cost than alternatives. For controlled comparison, this study presents multiple case studies and employs case analysis to compare the proposed Function–Structure–Economy (FSE) framework with traditional empirical methods in the same real-world scenarios. As a new principle framework, it provides the missing link between sensor technology, structural engineering, and value-based investment, thereby advancing the intelligent and scalable deployment of seismic monitoring systems.