Construction Schedule Research Articles

Комбинированный метод планирования релизов в Agile-командах на основании генетических алгоритмов

The paper analyzes the practices of release planning and task allocation in agile project management using multi-criteria optimization methods and genetic algorithms. A combined method that solves the problem of selecting re-quirements for release (Next Release Problem) based on NSGA-II algorithm and optimization of task scheduling con-sidering resource constraints and sequence of stages (Resource Constrained Project Scheduling Problem) is developed. The peculiarities of using DEAP library in Python to implement evolutionary computations are described, as well as the use of NumPy library to accelerate vector operations and Matplotlib to visualize the results. The architecture of the software solution is presented, including modular code organization and integration of custom heuristics for building optimal schedules. The description of utility, risk and penalty functions for dependency violations and resource con-straints is given, as well as the application of crossover and mutation methods for dictionary structures of schedules. Iterative optimization linking the stages of epics selection and schedule construction to reach a stable optimum is shown. Experimental validation confirmed the effectiveness of the proposed approach as it achieves utility maximiza-tion of the set of epics, minimization of penalties and idle time of employees in constructing optimal schedules. The developed system is applicable for complex release scheduling in Agile teams and forms the basis for further research in agile project management and resource optimization.

Force Feedback Method for Statically Indeterminate Steel Structure Construction Based on Staged Temperature Measurement.

Structural design usually adopts uniform temperature action. However, during the actual construction of the structure, the temperature field acting on the structure is inhomogeneous. Therefore, the simulation of the construction of statically indeterminate steel structures considering only the uniform temperature field cannot truly reflect the temperature action after structural molding and the evolution of the stress performance of the temporary stress system of structural construction. This paper proposes a force feedback method for the construction of statically indeterminate steel structures based on staged temperature measurements. In this method, the construction sequence and the measured temperature are taken into account, and the structural response under the action of temperature is obtained by using the first processing method in the matrix displacement method and the deformation coordination formula to obtain the deformation and stress values of the structure. The application results show that the structural forces calculated by the method proposed in this paper are closer to the measured data in the field, and the error is reduced by 10~40%. This paper provides a reference for the calculation of construction forces in statically indeterminate steel structures considering the effect of temperature inhomogeneity and provides a basis for construction safety assessment and schedule management.

Multi-Objective Optimal Scheduling for Microgrids—Improved Goose Algorithm

Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority.

The Relationship Between Artificial Intelligence (AI) and Building Information Modeling (BIM) Technologies for Sustainable Building in the Context of Smart Cities

The development of information technologies has been exponentially applied to the architecture, engineering, and construction (AEC) industries. The extent of the literature reveals that the two most pertinent technologies are building information modeling (BIM) and artificial intelligence (AI) technologies. The radical digitization of the AEC industry, enabled by BIM and AI, has contributed to the emergence of “smart cities”, which uses information technology to improve urban operational and sustainable efficiency. Few studies have investigated the roles of AI and BIM in AEC from the perspective of sustainable buildings in assisting designers to make sustainable decisions at building and city levels. Therefore, the purpose of this paper is to explore the research status and future development trends in the relationship between AI and BIM-aided sustainable building in the context of the smart city to provide researchers, designers, and technology developers with potential research directions. This paper adopted a macro and micro bibliographic method, which is used to map out the general research landscape. This is followed by a more in-depth analysis of the fields of sustainable design, sustainable construction, sustainable development, and life cycle assessment (LCA). The results show that the combination of AI and BIM helps to make optimal decisions on materials, cost, energy, construction scheduling, and monitoring and promotes the development of sustainable buildings in both technical and human aspects so to achieve Sustainable Development Goals 7 (ensuring access to affordable, reliable, and sustainable modern energy for all), 9 (building resilient infrastructure, promote inclusive and sustainable industries, and foster innovation), 11 (building inclusive, safe, risk-resilient, and sustainable cities and human settlements), and 12 (ensuring sustainable consumption and production patterns). In addition, the combination of AI, BIM, and LCA technologies offers great potential to improve building performance, and the future development of AI and BIM integration should not only consider the sustainability of buildings but also consider the human-centered design concept and the health, safety, and comfort of stakeholders as one of the goals to realize the multidimensional development of smart city based on city information model.

Surrounding Rock Squeezing Classification in Underground Engineering Using a Hybrid Paradigm of Generative Artificial Intelligence and Deep Ensemble Learning

Surrounding rock squeezing is a common geological disaster in underground excavation projects (e.g., TBM tunneling and deep mining), which has adverse effects on construction safety, schedule, and property. To predict the squeezing of the surrounding rock accurately and quickly, this study proposes a hybrid machine learning paradigm that integrates generative artificial intelligence and deep ensemble learning. Specifically, conditional tabular generative adversarial network is devised to solve the problems of data shortage and class imbalance for data augmentation at the data level, and the deep random forest is built based on the augmented data for subsequent squeezing classification. A total of 139 historical squeezing cases are collected worldwide to validate the efficacy of the proposed modeling paradigm. The results reveal that this paradigm achieves a prediction accuracy of 92.86% and a macro F1-score of 0.9292. In particular, the individual F1-scores on strong squeezing and extremely strong squeezing are more than 0.9, with excellent prediction reliability for high-intensity squeezing. Finally, a comparative analysis with traditional machine learning techniques is conducted and the superiority of this paradigm is further verified. This study provides a valuable reference for surrounding rock squeezing classification under a limited data environment.

Efficient Schedule Construction for Distributed Execution of Large DNN Models

Efficient Schedule Construction for Distributed Execution of Large DNN Models

Enhancing Construction Management Digital Twins Through Process Mining of Progress Logs

Large-scale infrastructure projects involve numerous complex processes, and even small construction management (CM) deficiencies can lead to significant resource waste. Digital twins (DTs) offer a potential solution to the management side of the problem. The current DT models focus on real-time physical space mapping, which causes the fragmentation of process data in servers and limits lifecycle algorithm implementation. In this paper, we propose a DT framework that integrates process twins to achieve process discovery through process mining and that serves as a supplement to DTs. The proposed framework was validated in a highway project. Based on BIM, GIS, and UAV physical entity twins, construction logs were collected, and process discovery was performed on them using process mining techniques, achieving process mapping and conformance checking for the process twins. The main conclusions are as follows: (1) the process twins accurately reflect the actual construction process, addressing the lack of process information in CM DTs; (2) process variants can be used to analyze abnormal changes in construction methods and identify potential construction risks in advance; (3) sudden changes in construction nodes during activities can affect resource allocation across multiple subsequent stages; (4) process twins can be used to visualize construction schedule risks, such as lead and lag times. The significance of this paper lies in the construction of process twins to complement the existing DT framework, providing a solution to the lost process relationships in DTs, enabling better process reproduction, and facilitating prediction and optimization. In future work, we will concentrate on conducting more in-depth research on process twins, drawing from a wider range of data sources and advancing intelligent process prediction techniques.

Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review

There are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML) applications in the process of construction productivity modeling (CPM) for construction labor productivity (CLP) and construction equipment productivity (CEP) from dataset acquisition to data analysis and evaluation, which includes their trends and applicability. An extensive analysis of 131 journals focused on the application of machine learning in construction productivity (ML-CP) from 1990 to 2024 via a mixed review methodology (bibliometric analysis and systematic review) was conducted. It can be concluded that despite the rise in automated dataset collection, the traditional method has its advantages. The review further found that the selection of ML models relies on each particular application, available data, and computational resources. Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. This study will supplement the insights gained in the review with a comprehensive understanding of how ML applications operate at each stage of CPM, enabling researchers to make future improvements.

Multi-objective time-cost-safety risk trade-off optimization for the construction scheduling problem

PurposeThe purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently.Design/methodology/approachIn this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities.FindingsThe results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies.Originality/valueThis research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.

A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints

Artificial island construction is a multifaceted engineering endeavor that demands precise scheduling to optimize resource allocation, control costs, ensure safety, and minimize environmental impact within dynamic marine environments. This study introduces a comprehensive multi-objective optimization model that integrates critical factors such as resource limitations, task dependencies, environmental variability, safety risks, and regulatory compliance. To effectively address the complexities of this model, we develop and employ the Multi-Objective Adaptive Cooperative Evolutionary Marine Genetic Algorithm (MACEMGA). MACEMGA combines cooperative coevolution, adaptive dynamic weighting, dynamic penalty functions, and advanced genetic operators to navigate the solution space efficiently and identify Pareto optimal schedules. Through extensive computational experiments using data from the Dalian Bay Cross-Sea Traffic Engineering project, MACEMGA is benchmarked against algorithms such as NSGA-II, SPEA2, and MOEA/D. The results demonstrate that MACEMGA achieves a reduction in construction time from 32.8 to 23.5 months and cost savings from CNY 4105.3 million to CNY 3650.0 million while maintaining high-quality outcomes and compliance with environmental standards. Additionally, MACEMGA shows improvements in hypervolume by up to 15% over existing methods and a Convergence Rate that is 8% faster than MOEA/D.

Prefab: New Technologies and Prospects of the Construction Industry

Statement of the problem. The main factors in the construction industry are: the cost of the project, its duration and uniqueness. Their achievement is facilitated by the currently developing pre-fabrication, the essence of which is reduced to the factory or factory manufacture of parts with their subsequent assembly into structural elements, modules and objects in general. At the same time, technologies, organization of construction and management of prefab processes have not been worked out to date. Results. A systematic review of the implementation of buildings using prefab technology with high factory readiness is proposed. An overview of modules that can be integrated into on-site projects with less waste and higher quality control compared to projects that use a traditional type of construction is provided. The difference between the prefab method and the modular assembly of a construction object is emphasized. An overview of the prefab products currently on the market is given. Conclusions. The article substantiates the directions of improving the quality of manufactured buildings inherent in prefab technologies, as well as the environmental friendliness and energy efficiency of this technology. A fragment of a BIM model with a calendar and construction schedule for the construction of a prefab house, prefab technologies used in Russia and in particular in Novosibirsk, is presented.

Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

Analisis Manajemen Konstruksi Pada Pembangunan Ruas Jalan Rate-Rate Dalam Kota

Road infrastructure development is an important factor in supporting economic growth and community mobility in urban areas. However, road construction in urban areas is often faced with various challenges, such as limited space, population density and the dynamics of community activities. This requires effective and efficient construction management to ensure the smoothness and success of the project. The aim of this research is to find out how cost calculations are carried out using AHSP and also to find out how to carry out and time schedule construction work for inner city lane roads. The method for implementing the construction of lane roads in the city is in the form of asphalting roads with a length of 3.06 KM and a duration of 90 days. The budget plan for building a 3.6 KM inner-city toll road is IDR. 10,428,920,000.

Automatic Planning Method of Construction Schedule under Multi-Dimensional Spatial Resource Constraints

To enhance the application of building information modeling (BIM) in schedule management for engineering projects, improve spatial planning capabilities, and minimize subjective factors in schedule preparation, this paper presents a framework and method for automatic construction schedule planning based on BIM and data-driven approaches under multi-dimensional spatial resource constraints. The method includes data acquisition, construction process reasoning, time parameter calculation, and schedule generation and scheduling. Initially, the construction data from the BIM model and manual input are imported into the reasoning system to establish the logical relationships of the construction process. Construction time parameters and spatial resource requirements are then automatically calculated using MySQL. Subsequently, the spatial layout and resource allocation data exported from the BIM model are compared to assess whether the space meets the project requirements. Finally, a construction schedule that satisfies the space constraints is generated. The method was validated through a case study, demonstrating its effectiveness in automatic schedule planning under human-computer interaction. This study introduces an innovative approach that considers spatial conflicts in the automatic scheduling process, improving planning efficiency and reducing the risk of spatial conflicts on construction sites.

Optimizing epoxy asphalt tack coat formulations for improved adhesion and working performance of ultrathin wearing course

Insufficient adhesion between ultrathin wearing courses (UTWCs) and substrate layers can result in pavement distress. Epoxy asphalt exhibits potential as a tack coat due to its high bonding strength and thermal stability. However, its rapid curing may pose challenges to construction schedules. This study aimed to optimize the formulations of epoxy asphalt tack coats (EATCs) to enhance the interfacial adhesion and working performance of UTWCs. The adhesion of EATCs was evaluated through rotational viscosity and tensile tests. Design parameters were optimized using analysis of variance and microscopic tests. Road performance was assessed via interlayer shear and tensile testing on three pavement structures. The results indicated that the curing agent strongly influenced the properties and curing kinetics of EATCs. Formulations containing complex polysebacic polyanhydride (CPSPA-EA) exhibited superior bonding while meeting construction schedules. Microscopic analysis of CPSPA-EA revealed a stable crosslinked network formed at 30% binder dosage. The amount of spraying significantly impacted the adhesion in UTWC structures. The optimal spraying amounts were 0.60 kg/m2 for asphalt concrete (AC-13), 0.55 kg/m2 for stone matrix asphalt (SMA-13), and 0.40 kg/m2 for cement concrete substrate layers. These findings presented an optimization framework for designing EATCs to enhance UTWCs working and long-term performance through balanced tack formulations and application parameters.

Dynamic Spatiotemporal Scheduling for Construction Building Projects

For building projects, the manager is responsible for coordinating the work of subcontractors at the construction site. This includes operations, material flows, and storage. In summary, one of their main roles is to ensure smooth team rotation, maintain fluid circulation, and avoid congestion or relaxation on the site. However, traditional tools lack the ability to consider the planning and management of worksite spaces when calculating the execution schedule and critical path. Consequently, three-week planning is usually carried out separately on independent plans, often using spreadsheets. In addition, a construction site is highly dynamic and mobile in nature, and the positioning of resources and workers can change daily. This makes the management of available space even more complex, and effective space management becomes an imperative. To address this challenge, this paper develops visual dynamic artifacts that present different operation types. The methodology and the conceptual framework facilitate the calculation of the Occupancy Rate (OR) that enables construction project managers to create simple yet dynamic spatiotemporal models of the construction schedule. By incorporating factors such as crew turnover and occupancy evolution, managers can simplify the calculation process and effectively optimize construction work by utilizing site occupancy rates. In summary, this paper presents the Dynamic Model of the Occupancy Rate Schedule (DMORS), a methodology developed through design science. This model utilizes created artifacts representing various operation types to ensure accurate calculations of dynamic occupancy by floor and sector in a site. Consequently, it enables the construction of a more realistic schedule based on critical space ideologies. The DMORS enables managers to use the OR for different floors and sectors of a site, allowing for better space management. A proof of concept demonstrates that this tool can enhance the efficiency and productivity of construction projects by optimizing crew schedules and resource allocation based on site OR.

Research Trends on Early Contractor Involvement in Construction Projects: A Bibliometric Analysis

The present study aims to highlight most significant journals in ECI research, most productive authors on ECI studies and territories, geospatial network of the active ECI research countries, current trend in ECI studies, and the most relevant keywords and co-occurrence networks in ECI studies. This study comprehensively examined the ECI research field by conducting a bibliometric analysis of 210 journal articles between 1992 and 2023 from the Web of Science (WoS) database. The findings showed that, Transportation Research Record, Construction Management and Economics, Journal of Management in Engineering, and Journal of Construction Engineering and Management are the most represented journals. Per Erik Eriksson is the most productive author identified in this study. United States of America, England and Australia are the most productive countries on ECI research. Current trends in ECI studies include ECI and project delivery, ECI as critical success factors, ECI in design and its impact on construction schedule performance, and overcoming cost-related issues through ECI. The highest co-occurred keywords are "procurement", "early contractor involvement", “performance”, and “construction”. To the best of the authors’ knowledge, this study is the first to conduct bibliometric analysis of past studies on ECI. As a result, the study contributed to the existing knowledge by identifying the pattern and trend of ECI research area, revealing its broader themes of research and clusters, mapping the network of key ECI authors and territories and recommending areas for future research.

A dual-adaptive directed genetic algorithm for construction scheduling

The precise and efficient generation of construction sequences is a crucial concern within the engineering field. However, the formulation of construction schedules heavily depends on the expertise and proficiency of project planners, leading to significant potential for inefficiencies and instability in project management. To deal with this challenge, this study automatically extracts constructability constraints from 3D models and proposes a dual-adaptive directed genetic algorithm (DADGA) to generate a structurally stable installation sequence. The proposed algorithm adaptively changes both the crossover and mutation probabilities based on the quality of individuals and evolutionary stages. In addition, the idea of directionality and the chief strategy artificially controls the direction of evolution, which greatly improves the efficiency and robustness of local search. The results of comparison experiments demonstrate that the DADGA outperforms the traditional genetic algorithm in terms of both efficiency and accuracy, and a practical example is also presented to showcase the capability of the DADGA in solving ultra-complicated construction scheduling problems.

Linear Scheduling Method-Based Multi-Objective Optimization for Off-Site Construction Manufacturing

Optimal production scheduling in off-site construction is hindered by the lack of comprehensive methods that simultaneously addresses workforce allocation, realistic labor productivity, workstation idle time (WIT), work-in-progress (WIP), and project completion time (PCT). To overcome these limitations, this paper introduces a hybrid planning and scheduling method that integrates the linear scheduling method reflecting workforce allocation, Monte Carlo simulation for estimating realistic labor productivity, and a multi-objective optimization model to minimize WIT, WIP, and PCT concurrently. The proposed method is validated using the Taillard instance benchmark dataset, and a panelized production line located in Edmonton, Canada. As a result, the proposed method is effective in generating realistic and optimal production schedules, achieving reductions of 5.2 % in PCT, 41.45 % in WIT, and 56.42 % in WIP. These findings are crucial for industry professionals aiming to improve efficiency in the production line and provide a foundation for further research in off-site construction scheduling.

Predicting construction schedule performance with last planner system and machine learning

Construction project schedules deviate due to uncertainty and variability unless timely actions are implemented. While the limitations of traditional management practices help to facilitate such assessments have been widely covered, quantitative LPS research has shown empirical relations between its indicators, performance, and outcome. This paper creates a model to predict the schedule outcome during early execution using the LPS metrics and the Design Science approach. 18 solution artifacts were evaluated to predict three schedule outcome variables using 15 indicators in 1464 sample points collected from nine subsequent execution intervals from 164 projects. The selected artifact predicted the schedule outcome, which is a combination of the schedule performance at planned completion and the actual schedule deviation at completion, with a MAE of 1.24% and R2 = 0.96 averaged across the nine execution intervals, using solely LPS indicators. The model can be applied as an early warning mechanism in LPS IT-Support software.