Smart Construction

ISSN: 2960-2025 (Print)

ISSN: 2960-2033 (Online)

CODEN: SCABAK

CiteScore 2025: 1.5

About This Journal
Special Issues
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AI for Construction Materials Innovation: from Design to Performance
Special Issue Editor:   Xiaohong Zhu, Xingquan Wang, Zhi Cheng, Ruoqi Zhao
Submission Deadline:  31 October 2027
Building Resilience and Sustainability in Civil Engineering with Smart Construction
Special Issue Editor:   Mohd Rosli Mohd Hasan, Hui Yao, Ali Jamshidi, Seyed Reza Omranian
Submission Deadline:  31 August 2026
Topic Collections
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Construction Site Monitoring and Optimization using Digital Twins
Topic Collection Editor:   Byungjoo Choi, Muhyiddine Jradi
Intelligent Condition Assessment and Performance Prediction Towards Resilient and Sustainable Pavement Structure
Topic Collection Editor:   Tao Ma, Songtao Lv, Zhen Leng, Yuqing Zhang, Siqi Wang, Hui Yao
Latest Articles
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Experimental investigation on cohesion-friction mechanical properties for early-age concrete
Dechun Lu,Zhiyuan Guo,Tao Cai,Guosheng Wang,Zhiwei Gao,Xiuli Du
Article22 Jun 2026OPEN ACCESS

As a core material in modern construction, the early-age properties of concrete have a decisive impact on the safety and durability of civil engineering structures. However, systematic research on the mechanical properties of early-age concrete remains limited, particularly regarding the combined influence of cohesive and frictional properties on the material’s macroscopic mechanical behavior, which has not been thoroughly explored. To address this gap, this paper employs a decoupling method for testing the cohesion-friction mechanical properties of concrete, as proposed in previous work. This method successfully separates the cohesive and frictional properties of early-age concrete, validating its applicability under early-age conditions and obtaining typical failure modes following material performance degradation. Furthermore, by analyzing the evolution patterns of cohesive and frictional properties during deformation and strength development, the synergistic mechanism of cohesion-friction mechanical properties in early-age concrete was revealed. The results indicate that the responses of cohesive and frictional properties to hydrostatic pressure in early-age concrete exhibit significant differences. The reduction in macroscopic shear strength and stiffness is fundamentally attributed to the irreversible dissipation of cohesive strength. Ultimately, the mechanical behavior of early-age concrete gradually approaches that of granular materials without cohesion.

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A multi-criteria decision framework for selecting preventive maintenance measures on asphalt pavement: a case study of the Liuzhou North Ring Expressway
Zhen Wu,Mohd Rosli Mohd Hasan,Oumar Orozi Sougui,Diyar Khan,Hainian Wang,Hui Wang
Article16 Jun 2026OPEN ACCESS

In recent years, the rapid development and proliferation of highways in China have made asphalt pavement maintenance increasingly complex, requiring maintenance management departments to make practical choices of preventive maintenance measures within limited budgets. To improve comprehensiveness, scientific rigor, and the economy of decision-making, the Analytic Hierarchy Process (AHP) was employed to conduct a decision-optimization study of preventive maintenance measures for asphalt pavements. Taking the preventive maintenance project of the Liuzhou North Ring Expressway in Guangxi as a case study, maintenance measures were initially selected through road condition assessment and investigation. A multi-level, multi-objective decision-making AHP model was constructed, including an objective layer, a criterion layer, an indicator layer, and a scheme layer. By comprehensively considering maintenance needs and assigning values to multi-level factors, the weights and priorities of each maintenance measure were determined. The results show that the ranking and weight calculation of measures such as ultra-thin cover, composite seal coat, micro-surfacing, thin layer cover, and seal coat are relatively rational, and the theoretical analysis results are in good agreement with actual needs.

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Statistical evaluation of cementitious composites containing rice husk ash and recycled steel fibers
Kaushal Kumar,Prawar Chaudhary,Anil Dhanda,Preeti Rustagi,Roopsi Rathee,Rishabh Arora,Karan Gehlot,Ashhad Imam,Nishant Yadav
Article16 Jun 2026OPEN ACCESS

This study presents a statistical evaluation of cementitious composites incorporating rice husk ash (RHA) and recycled steel fibers using a structured experimental design. A Central Composite Rotatable Design (CCRD) within the framework of Response Surface Methodology (RSM) was employed to examine the combined influence of RHA content, recycled steel fiber aspect ratio, and water–cement ratio on selected properties of concrete. A total of twenty experimental mixes were prepared according to the design matrix, and compressive strength, flexural strength, and water absorption were measured as response variables. Material characterization was limited to X-ray fluorescence–based oxide composition for cement and RHA and scanning electron microscopy–energy dispersive spectroscopy (SEM–EDS) based morphological documentation for RHA. The experimental results were analyzed using analysis of variance to identify statistically supported trends and interaction effects within the investigated parameter ranges. The findings indicate that strength-related responses and water absorption are governed primarily by interaction effects among mixture parameters rather than by individual variables acting independently. The results are interpreted within the investigated design space (10%–20% RHA replacement), and no comparison with control mixtures (0% RHA) is implied. This study provides statistically supported, trend-level insights into the behavior of RHA- and recycled steel fiber–modified cementitious composites under the defined experimental conditions. The results contribute experimental and statistical evidence relevant to structural engineering applications where controlled modification of concrete mixtures is of interest.

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AIstructure-Copilot: assistant for generative AI-driven intelligent design of building structures
Sizhong Qin ,Wenjie Liao ,Shengnan Huang ,Kongguo Hu ,Zhuang Tan ,Yuan Gao ,Xinzheng Lu
Article04 Mar 2024OPEN ACCESS
The rapid advancement of intelligent design technology in building structures has been primarily implemented in engineering practice through the use of local or cloud-based software to offer intelligent design services. However, local intelligent design services are time-consuming and require high-end hardware, whereas cloud-based designs fail to integrate seamlessly with existing design processes. Consequently, providing convenient intelligent design support for engineering practices is challenging. To address these problems, this study proposes a local–cloud collaborative intelligent design technology called AIstructure-Copilot, which serves as a structural intelligent design assistant. In this system, the local end performs routine graphical operations that align with engineers' design habits, whereas the cloud end executes generative artificial intelligence (AI) for intelligent design, thereby enhancing efficiency and effectively combining the strengths of both services. Specifically, this technology achieves a high level of automation and intelligence throughout the entire process, encompassing architectural design, structural design, and the establishment and execution of structural analysis models. This is accomplished by constructing a local–cloud collaborative mode, introducing a comprehensive data transmission format, and developing a cloud interface for generative AI algorithms. The effectiveness of the AIstructure-Copilot model was validated using a typical case study. The results demonstrate that AI design improves design efficiency by more than tenfold, satisfies the regulatory requirements of design schemes, and exhibits a discrepancy of approximately 20% when compared with designs created by competent engineers.
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Frontier AI in computational civil engineering: a review of graph, sequence, physics-informed deep learning, and beyond (2020–2025)
Linghan Song,Jiansheng Fan,Shenxiang Zeng,Chen Wang
Review26 Jan 2026OPEN ACCESS

Structural computational analysis in civil engineering increasingly demands efficient, robust, and physics-aware methodologies capable of addressing non-Euclidean geometries, history-dependent behaviors, and multi-scale problems that remain challenging for conventional numerical approaches. Recent advances in frontier artificial intelligence (AI) techniques have shown promising potential to overcome these limitations. This paper presents a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) and Transformer-based architectures, and physics-informed methods. We synthesize fundamental concepts, typical model variants, and representative applications across diverse tasks, including constitutive modeling, static and dynamic structural analysis, data reconstruction, and parameter inversion. Furthermore, we identify critical research gaps and discuss potential future directions within each model family. A quantitative analysis of the reviewed studies is conducted, categorizing them by publication year, application task, and adopted model type. Common challenges regarding benchmarking, empirical–physics trade-offs, scalability and generalizability are summarized. Finally, we highlight several promising techniques for advancing intelligent structural computation and promoting practical engineering deployment.

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Can ChatGPT assist in cost analysis and bid pricing in construction estimating? A pilot study using a bridge rehabilitation project
Alireza Ghasemi ,Fei Dai
Article04 Sep 2024OPEN ACCESS
As the large language model Generative Pre-trained Transformer 4 (GPT-4) recently came into being and has attracted much attention, this study examined its efficacy in analyzing the cost of work items and estimating bid prices in construction estimating. This study utilized a rehabilitation project for the Beaver Dam Road Bridge in Pennsylvania, USA as a case study. The authors integrated ChatGPT-4 to handle bid pricing for five specific work items: concrete and formwork, reinforcement, structure backfill, membrane waterproofing system installation, and borrow excavation. Prior knowledge regarding production rates, labor hourly rates, equipment rates, and material rates was used as input. Prompts and instructions were established for interactive execution of the cost estimation. The model's outputs were compared with the ground truth and the bids from three bidders available at Pennsylvania Department of Transportation (PennDOT)’s website. The comparative analysis revealed that GPT-4 holds the potential for construction estimating with reasonable accuracy. However, it is also essential to recognize the consistency and reliability issues that may exist, which would affect ChatGPT’s performance in new scenarios.
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