Precast Concrete Components Research Articles

Production Sequencing and Layout Optimization of Precast Concrete Components under Mold Resource Constraints

Precast concrete components have attracted a lot of attention due to their efficient production on off-site production lines. However, in the precast component production process, unreasonable production sequence and mold layout will reduce production efficiency and affect the workload balance between each process. Due to the multi-species and small-lot production characteristics of precast concrete components, the number of molds corresponding to each precast concrete component is generally limited. In this paper, a production sequence and layout optimization model for assembling precast concrete components under a limited number of molds is proposed, aiming to improve the comprehensive utilization efficiency of the mold tables and balance the workload between each production process of precast components. In order to obtain a better production sequence and a richer combination of mold layout schemes, a multi-objective teaching-learning-based optimization algorithm based on the Pareto dominance relation is developed, and an enhancement mechanism is embedded in the proposed algorithm. To verify the superior performance of the enhanced teaching-learning-based optimization algorithm in improving the comprehensive utilization efficiency of the mold tables and balancing the workload between various processes, three different sizes of precast concrete component production cases are designed. The research results show that the proposed model and optimization algorithm can help production managers to efficiently formulate more reasonable precast component production sequence and layout schemes, especially for those enterprises that are struggling to improve the efficiency of precast concrete component production.

Automated optimization for steel mold of precast components based on multi-objective evolutionary algorithm

Designing an economical and reliable steel mold for precast concrete (PC) components can accelerate the promotion of prefabricated buildings. This paper presents an automated design framework for a steel mold of PC components to generate manufacturing information, including dimensions and location of each plate. The profile and dimensions of PC components are extracted from the shop drawings, and a finite element model of the steel mold is established and initialized. A multi-objective optimization model considering material usage and manufacturing convenience is developed to determine the geometry and quantity of plates in steel molds. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to obtain a Pareto solution set for steel mold design. The production information in DXF format can be automatically generated by parsing Industry Foundation Classes (IFC) model. The cost-effectivity and practicality of the proposed framework were verified via the design case for a real PC stair mold.

Precast concrete component yard layout optimization considering safety and efficiency

ABSTRACT The contradiction between the space demand for precast concrete components (PCCs) and the limited area of the construction site is reinforced by the increase in the prefabrication rate of prefabricated buildings, and a large number of PCC hoisting operations may have an adverse impact on the safety and efficiency of the construction site. Therefore, the importance of precast concrete component yard layout planning (PCCYLP) is becoming increasingly prominent. Most existing research on construction site layout planning (CSLP) has focused on all temporary facilities on the construction site and has neglected the particularity of PCC when solving the above problems. Based on this, this paper integrates the PCC hoisting efficiency and safety into the existing CSLP research and optimizes PCCYLP solutions by establishing a PCCYLP model. The model proposes a non-centralized PCC yard division method. Moreover, the surplus rectangle algorithm is used to calculate the yard sizes, which are input into the model in alternative ways. Engineering cases verify the effectiveness of the proposed model, and the non-dominated sorting genetic algorithm is selected for simulation. The results show that the method can enhance the scientific and reasonable layout solution and provide a favorable guarantee for subsequent safe and efficient construction activities.

Optimized transportation scheduling for precast concrete components considering heterogeneous vehicle-size matching

Recently, prefabricated construction has been vigorously promoted, resulting in high demand for precast concrete (PC) components. The transportation scheduling optimization problem of PC components with various kinds from multiple projects arises. Unlike conventional cargo, PC components are characterized by shape heterogeneity, large volume, and strict delivery time limits. Based on three characteristics, a heterogeneous fixed fleet vehicle routing problem (HFFVRP) for PC components is introduced, where heterogeneous vehicles, allocation of PC components to size-matching vehicles, and hybrid time windows are considered. Then a two-stage solution strategy based on the improved ant colony optimization (ACO) and Dijkstra algorithm is designed to obtain optimal vehicle routes under minimum transportation costs. The results indicate that the improved ACO-Dijkstra algorithm outperforms in obtaining optimal transportation plans for heterogeneous vehicles compared with manual decision-making and other heuristic algorithms. Sensitivity analysis denotes that utilizing heterogeneous vehicles contributes to reductions in transportation costs, and vehicle configuration should be adjusted along with demand scales. The proposed model and algorithm extend the theoretical basis of construction industrial applications.

An optimization model for the duration of workshop production processes for precast concrete components for highway projects

Purpose of this Paper Highway projects have tight schedule requirements, huge components and high reliance on labor, which are notorious for making the scheduling of component production difficult. This paper thus proposes an approach to optimize the production of large precast components for highway projects to reduce the production duration. Design/Methodology/Approach A model for optimizing the duration of precast concrete component production was developed by considering the large size of the components and the limited space of workshops. The optimized solution was obtained by using a sine cosine algorithm (SCA). A real case study was conducted to verify the feasibility of the model. Findings The production time for assembling reinforcement and concreting was significantly reduced and suggested a further mechanized production instead of manual production for assembling reinforcement. Planning of the tonnage and trips of the torpedo tank will be necessary to increase the concreting efficiency. Originality/Value This paper is expected to provide empirical references for planning and optimizing the production of precast concrete components for highway projects and other mega-infrastructures. The optimization model is encouraged to improve the efficiency of the production of large precast concrete components and further promote the development of highway projects.

A Robotic Solution for Precision Smoothing and Roughening of Precast Concrete Surfaces: Design and Experimental Validation.

Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot's escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

Bond-slip behavior of steel reinforcing bars in ultra-high performance concrete for field-cast connection of precast bridge decks

In Accelerated Bridge Construction (ABC), Ultra-High Performance Concrete (UHPC) is often used for connecting precast concrete bridge components, including deck portions of the Deck Bulb-T girders. An alternative low-cost non-proprietary UHPC has been proposed for use in place of the proprietary UHPC for connecting the precast components. The pullout behavior of steel reinforcing bars in closure pour with typical range for embedment lengths is studied for both proprietary and non-proprietary UHPC materials.

Acoustic Emission Monitoring for Damage Assessment of a Magnetite Ultra-High-Performance Concrete (MUHPC) Block in a Bending Test

Ultra-high-performance concrete (UHPC) is widely used because of its exceptional properties, such as high compressive and flexural strength, low permeability, and resistance to abrasion and chemical attack. It is commonly employed for intricate constructions like skyscrapers, precast concrete components, and infrastructure. Nevertheless, the incorporation of appropriate fibers into UHPC is carried out in order to accomplish objectives such as augmenting strength, enhancing toughness, and regulating cracking. This study employed magnetite as an additive to a UHPC block in order to examine the mechanical characteristics of a newly cast UHPC block. Acoustic emission was employed to evaluate the damage to the UHPC block for tracking purposes. Acoustic emission is a non-invasive testing technique that does not cause harm to the specimen when it is exposed to a load. On the basis of this, many critical locations that indicated the propagation of cracks were analyzed, as well as various loading stages across the specimen. The b-value is a method that can evaluate the extent of damage by analyzing the amplitude distribution. Distinct paths of b-values were noted for each loading stage, indicating major damage scenarios based on their slopes.

Hydration, strength, and microstructure evolution of Portland cement-calcium sulphoaluminate cement-CSH seeds ultra-early strength cementitious system

Improving the very early age strength of precast concrete components at ambient temperature and ensuring the development of later strength is of significant engineering importance. This objective was successfully achieved by incorporating a specific percentage of 0–3% CSH seeds (CSHs) and 5%-20% calcium sulphoaluminate cement (CSA) into Portland cement (PC). The hydration, strength and microstructure evolution of Portland cement-calcium sulphoaluminate cement-CSH seeds cementitious system were investigated. Hydration heat tests reveal that CSHs effectively accelerated the early hydration of the slurry within the first 12 h without affecting its later hydration. CSHs and CSA acted synergistically to enhance hydration kinetics. However, it is crucial to control the proportion of CSA within the range of 5%-10%; exceeding this ratio can severely inhibit the hydration of silicates. Strength tests indicate that the addition of CSHs and CSA can increase the compressive and flexural strengths of mortar within the first 12 h by 282% and 208%, respectively, without impacting the development of its later-age strength. Together, they synergistically provide favorable support for the strength and toughness of matrix. Based on linear regression analysis and nitrogen adsorption and desorption tests, it was concluded that CSHs enhance the hydration dependence of matrix strength, while CSA diminishes this characteristic. This is because CSHs refine the pore size distribution of the paste, increasing the proportion of micropores, whereas CSA promotes the formation of more mesopores and macropores, leading to coarser pores; however, 5% CSA is beneficial for further refining the pore structure. The microcosmic phase evolution of the early-strength cementitious system was analyzed through X-ray diffraction and scanning electron microscope-energy dispersive spectrometer experiments. Results demonstrate that low proportions of CSA and CSHs promote the co-growth of ettringite and C-S-H gel, ensuring a more rapid formation of a dense hydration network in the early stages, thereby accelerating strength development. These findings provide a reference for the design and preparation of precast concrete.

Displacement analysis of a geogrid-reinforced retaining wall with modular precast concrete facing

Reinforced earth retaining walls are widely used in transportation projects, such as embankments, bridge access roads, and bridge abutments. When the wall height is large, the weight of the embankment soil and vehicle loads can cause large wall displacements, thereby affecting the serviceability of the wall system. Although many retaining wall projects use wall-facing structures assembled from precast concrete components, most domestic studies have focused on continuous reinforced concrete wall-facing structures. Therefore, to understand the displacement behavior of reinforced retaining walls when the wall height is large, we investigated the displacement behavior of an earth retaining wall in the My Phuoc-Tan Van overpass project, Binh Duong Province. This study uses a finite element simulation that considers the nonlinear behavior of backfill soil, foundation soil, geotextiles, and the interaction between precast concrete units.

Precast concrete project image dataset for deep learning object detection

Computer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88–0.97, average robustness of 0.84–0.96, and average detection speed of 3.64–23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.

Building an Information Modeling-Based System for Automatically Generating the Assembly Sequence of Precast Concrete Components Using a Genetic Algorithm

Facing a significant decrease in economic working processes, Off-Site Construction (OSC) methods have been frequently adopted in response to challenges such as declining productivity and labor shortages in the construction industry. Currently, in most OSC applications, the assembly phase is traditionally managed based on the personal experience and judgment of the site managers. This approach can lead to inaccuracies or omissions, particularly when dealing with a large amount of information on large, complex construction sites. Additionally, there are limitations in exploring more efficient and productive alternatives for rapidly adapting to changing on-site conditions. Given that the assembly phase significantly affects the OSC productivity, a systematic management approach is crucial for expanding OSC methods. Some initial studies used computer algorithms to determine the optimal assembly sequences. However, these studies often focused on geometrical characteristics, such as component weight or spatial occupancy, neglecting crucial factors in actual site planning, such as the work radius and component installation status. Moreover, these studies tended to prioritize the generation of initial assembly sequences rather than providing alternatives for adapting to evolving on-site conditions. In response to these limitations, this study presents a systematic framework utilizing a Building Information Modeling (BIM)–Genetic Algorithm (GA) approach to generate Precast Concrete (PC) component installation sequences. The developed system employs Genetic Algorithms to objectively explore diverse assembly plans, emphasizing the flexibility of accommodating evolving on-site conditions. Real on-site scenarios were simulated using this framework to explore multiple assembly plan alternatives and validate their applicability. Comprehensive interviews were conducted to validate the research and confirm the system’s potential contributions, especially at just-in-time-focused PC sites. Acknowledging a broader range of variables such as equipment and manpower, this study anticipates fostering more systematic on-site management within the context of a digitized construction environment. The proposed algorithm contributes to improving both productivity and sustainability of the construction industry by optimizing the management process of the off-site construction projects.

Microbial cement grout anchoring steel reinforcement pull-out test

Microbial cement, known for its superior fluidity, stable crystal formation, and strong bond with concrete, is an effective solution in fixing defects such as voids that appear due to insufficient grouting in the joints of precast concrete components. To evaluate the mechanical properties of rebar anchored with microbial cement grout, three pull-out tests were designed, taking into account parameters such as calcium source concentration, the filler in borehole voids, and the shape of the rebar. The results indicate that specimens with a higher concentration of calcium source require fewer grouting cycles, but the pull-out bearing capacity of the anchored rebar is lower. However, the introduction of quartz sand as a filler in the borehole voids results in a significant increase in the pull-out bearing capacity of the rebar compared to specimens without filler. Among these, the specimens with a medium particle size of 0.5 ~ 1 mm (1/4δ ~ 1/2δ, the gap of δ = 2 mm between the steel bars and hole wall) have the highest pull-out bearing capacity. In comparing rebar shapes, ribbed rebar slightly outperforms smooth round rebar in terms of pull-out bearing capacity. Based on the experimental results analysis, the anchoring mechanism of microbial cement grouting on reinforcement has been elucidated.

Research on Factors Influencing Precast Concrete Component Transportation Costs Based on System Dynamics

Research on Factors Influencing Precast Concrete Component Transportation Costs Based on System Dynamics

Modularisation Strategies for Individualised Precast Construction—Conceptual Fundamentals and Research Directions

Modular precast construction is a methodological approach to reduce environmental impacts and increase productivity when building with concrete. Constructions are segmented into similar precast concrete elements, prefabricated with integrated quality control, and assembled just-in-sequence on site. Due to the automatised prefabrication, inaccuracies are minimised and the use of high-performance materials is enabled. As a result, the construction process is accelerated, and the modules can be designed to be lightweight and resource-efficient. This contribution presents the fundamentals of modular constructions made from precast concrete components. Then, to elaborate the requirements of a contemporary modular precast construction, the historic developments are described. Further, concepts and technical processes–comprehensible to non-expert readers–are introduced to formalise the discussion about the current state-of-the-art methods. Three case studies treating ongoing research are introduced and related to the conceptual fundamentals. The research is evaluated with regard to current barriers and future directions. In conclusion, modular precast construction is able to reduce emissions and increase productivity in the sector if researchers and firms coordinate the development of suitable technologies that bring value to critical stakeholders.

Research on Vehicle Scheduling Optimization Model of Precast Concrete Components Considering Carbon Emission Cost

The distribution of precast concrete (PC) components has the characteristics of high time penalty costs, transportation costs, long-term accumulated carbon emission, and long unloading waiting time. These characteristics result in increased distribution costs. Aiming at this problem, the vehicle scheduling (VS) optimization model was proposed to minimize total distribution costs, including transportation costs, unloading waiting costs, carbon emissions costs, and so forth. The proposed model was solved using the genetic algorithm. Finally, the proposed model is verified by a simulation study. The main conclusions are: 1) A PC-component VS model aiming at minimum cost is established, which can provide a better scheme for PC-component VS on the basis of minimizing fixed cost, transportation cost, unloading waiting cost, penalty cost, and carbon cost. 2) This paper constructs a VS model that reflects the loading characteristics of PC components. The type of PC component is added as a judgment variable in the model, and the actual loading weight is calculated according to the type of PC component and the loading requirements. It improves the accuracy of PC component VS results and expands the PC component VS model. 3) Based on this model, some valuable parameters have been sensitively analyzed and some valuable conclusions have been drawn to provide some references for similar cases. For example, carbon emission increases with the increase of freight volume and so on. The result of this study is to provide a cost-minimization VS model with PC components.

Intelligent inspection of appearance quality for precast concrete components based on improved YOLO model and multi-source data

PurposeMonitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.Design/methodology/approachThe paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.FindingsThe improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.Originality/valueThe proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The moderating impact of organizational readiness, competitive pressure and compatibility on the cost of using precast concrete components

PurposeThe purpose of this study is to investigate the factors affecting the cost of concrete component prefabrication using the moderating role of organizational readiness, compatibility and competitive pressure.Design/methodology/approachThe information collected in this study was obtained from 188 questionnaires filled out online by civil engineers active in the Iranian construction industry. Data analysis was performed by partial least squares structural equation modeling.FindingsThe results reveal that perceived risk has the greatest impact on cost of using precast concrete components (PCC). Furthermore, the organizational readiness, competitive pressure and compatibility variables have moderating role in the relationship between perceived risk and perceived usefulness. The results can help policymakers and managers to apply the appropriate perspective and strategy to promote this method by illustrating the factors affecting the cost of using PCC in the building construction.Originality/valueUsing the variables of perceived risk, perceived usefulness, and perceived ease of use to predict factors affecting the cost of using PCC is considered as an innovation in this research. In addition to identifying the most important factors affecting the cost of PCC prefabrication in the construction industry, this research tries to identify the impact of environmental factors on the model by examining the moderating role of organizational readiness, competitive pressure and compatibility.

Virtual trail assembly of prefabricated structures based on point cloud and BIM

During the construction of prefabricated structures, manufacturing deviations and assembly deviations will accumulate. By detecting dimensional deviations of precast concrete (PC) components, rework can be avoided. This paper presents a Scan-vs-BIM (Building Information Modeling) method based on point clouds to identify PC components and evaluate manufacturing deviations. Based on Extended Orthogonal Procrustes Analysis (EOPA), a virtual trial assembly (VTA) approach is introduced to quantify cumulative deviation between PC components and the corresponding nominal specifications. The method has been applied to a two-story prefabricated frame, and the obtained results show that the average absolute deviation and standard deviation between the structure and the BIM model were 11.55 mm, and 3.60 mm, respectively. This study presents a solution for predicting the assembly deviation of PC components, and for enhancing the quality of prefabricated structures.

The capacitated location problem of precast concrete component factory

The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced.