A BIM-based automated site layout planning framework for congested construction sites
Introduction
Construction site layout planning (CSLP) is a crucial step in construction planning that has been proven to reduce material handling costs while improving safety and productivity of a project [1], [2], [3]. Construction projects require a large number of temporary facilities such as material storage areas, fabrication shops, etc. in order to support various construction activities. Traditionally these facilities are set up on unoccupied areas, within the boundaries of the construction site. In such situations the goal of CSLP is to determine the best arrangement of temporary facilities such that the travel distances of construction personnel is minimized [4], [5]. An obvious solution could be to set up temporary facilities on the free areas surrounding the building under construction. However, this is possible only on construction sites which have adequate amounts of free area to facilitate such an arrangement. In most urban construction projects, site space is limited and must be used judiciously in order to avoid problems with accessibility, safety and congestion. Comprehensive site layout planning can ensure a smooth flow of materials, equipment, and labour, thereby improving the safety and efficiency of on-site operations.
Site layout models fall into two categories – (1) static layout models, which assume that all of the facilities are assembled at the start and exist for the entire duration of construction [1], [4], [5], [6], [7], [8], [9], [10], and (2) dynamic layout models, which consider the actual duration for which facilities are required [11], [12], [13], [14], [15], [16], [17], [18]. Dynamic layout models are far superior to static models in generating optimum layout plans because they allow layout planners to cater to the changing site requirements and facilitate site space to be reused. Currently dynamic models are created specific to a project, based on the following information – (1) the number and types of facilities required, (2) the dimensions of each facility, and (3) the specific time interval for which each facility would be required on the construction site [19]. In most CSLP tools, such information has to be determined by the layout planner and manually entered into the software program. However manually determining this information could be quite laborious, especially for projects with complex schedules spanning several days. Changes to the design or construction plans would have to be continuously updated into the site layout models, resulting in an inefficient workflow that is very time consuming. This severely limits the practicality of current CSLP tools and is one of the reasons for their failure to achieve widespread adoption by the construction industry. There is a need for a practical and generic tool, which not only reduces unnecessary work by the layout planner but can also be easily adapted for use on different projects. Several research studies have attempted to improve the ease of use of dynamic CSLP tools. Tommelein et al. [2] developed a dynamic layout tool called MovePlan with a graphical user interface, which took activity relationships as input and generated optimized site layouts. Xing et al. [20] developed a GIS- based construction site material layout evaluation tool which took the resource loaded schedule as input to calculate the material accessibility grade on a construction site. Said and El-Rayes [21] developed a construction logistics optimization system, which automated the retrieval of spatial and temporal data from BIM models and construction schedules. In this study, we further improve on the practicality of current tools, by presenting a BIM based framework that automates the creation of mathematical models for dynamic CSLP. BIM models are rich sources of information and have been used to facilitate site layout planning [21], [22], [23], [24], [25], [26]. The focus of this paper is to leverage information from BIM models and construction schedules, to estimate the size, dimensions and number of temporary facilities required during different stages of construction. Since this methodology is pivoted on BIM, design and construction changes can be automatically integrated into the mathematical models, significantly reducing redundant work by layout planners.
In almost all of the studies on site layout planning, the optimization goal is to determine temporary facility layouts that would minimize on-site transportation costs, without compromising the safety or accessibility of the site. [1], [2], [3], [4], [5], [6], [7], [8], [9], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [24], [27], [28], [29], [30], [31], [32], [33], [34]. A common formula used to achieve this iswhere dij and fij represent the distance and frequency of trips between two facilities i and j, respectively, while n represents the total number of facilities. For the sake of simplicity, most early studies on CSLP approximated dij by using linear distances such as the Euclidean (straight line) or Manhattan (rectangular) distance. However, due to the presence of obstacles it is nearly impossible to always follow straight line paths, due to which the Euclidean and Manhattan distances would be significantly different from the actual travel distances of site personnel. Yahya and Saka [18] introduced the concept of obstruction distance, which was added to the computed Euclidean path in order to approximate the actual travel distances. Park et al. [30] demonstrated the benefits of using actual travel distances instead of linear distances in solving the floor-level material layout problem for an indoor environment. In our study, we use the A* algorithm to accurately compute the actual travel distances between facilities on a construction site, and use them as a basis for site layout optimization. Our method also considers variations in path widths between construction personnel and machines, thereby resulting in a more accurate representation of on-site transportation activities. Another drawback among all of the previous studies on CSLP is that the dimensions of facilities are taken as input parameters, prior to performing the optimization. As a result, only the position and orientation of each facility are considered as the decision variables for optimization. As will be demonstrated in this paper, the previous approach severely limits the range of possible solutions. In this study, we consider the position, orientation and dimensions of each facility as decision variables, which are then optimized using Genetic Algorithms (GA). As a result, our CSLP tool determines the optimal dimensions of each facility, significantly improving the efficiency of generated layouts. To facilitate the use of GA, a modified crossover and mutation operator has been developed in this study.
A characteristic of urban construction projects is the lack of on-site storage space. To make up for this shortage of space, layout planners may assign storage facilities to be set up inside the buildings under construction [31]. Such an approach increases the total area for setting up of facilities and consequently reduces on-site congestion. However, the interior regions of a building are active workspaces for a number of floor-level construction activities [35]. This imposes a limitation on the amount of space that can be used for interior storage. As a result, interior storage plans must be planned and coordinated carefully, to ensure maximum utilization of the available space. Park et al. [30] developed a system framework to optimize the interior storage locations of construction materials on every floor of the building under construction. However, their study was limited to optimizing storage locations in interior spaces only and did not address the storage needs in exterior regions of the construction site. Elbeltagi et al. [13] developed a dynamic CSLP tool, which used the constructed space of a building to store temporary facilities, with a view to reduce congestion. Said and El-Rayes [15] proposed a congested construction logistics planning (C2LP) model that generates optimal material logistics and site layout plans. The C2LP model requires input parameters such as the site exterior and interior spatial data, dimensions of temporary facilities, their relationship with activities on the construction schedule and material assignment to activities, based on which it optimizes the storage locations in exterior and interior building spaces. In a following study, Said and El-Rayes [21] developed an automated multi-objective construction logistics optimization system (AMCLOS), which uses information in BIM models and schedules to optimize the utilization of interior storage spaces in a building. The AMCLOS system uses IFC (Industry Foundation Classes) files to extract the geometry of interior and exterior site regions, thereby automating the computation of available storage space. However, the permissible periods of interior storage areas of the materials and dimensions of each temporary facility have to be manually specified in the AMCLOS system. In our study, we leverage information from BIM models to develop an automated method for interior and exterior storage optimization. At any particular instant of time, the amount of interior storage space is dependent upon the number of completed floors, the geometry of the building and the presence of floor-level construction activities. By linking material and spatial data from BIM models to activity data from the schedule, we are able to automate the computation of available interior storage space during different stages of the project. Our framework also automates the computation of required storage amounts for each material, and optimally assigns them to different storage locations. Therefore, this study presents an automated framework for CSLP, which addresses the requirements of congested construction sites by utilizing interior building spaces to store materials. Our framework, which relies on BIM, enables us to develop a CSLP model that is generic enough to be useful in a variety of cases. The number of inputs from layout planners is minimized since most of the computations are performed on information available from the BIM model and construction schedule.
Our framework for automated CSLP using BIM consists of three modules (see Fig. 1). In the first module, BIM based facility size estimation is used to accurately compute the required size and dimensions for each facility. In the second module, we present a methodology framework to automate the creation of dynamic layout models. The third module deals with formulating the objective function and using an actual travel path driven optimization to generate facility layouts. A demonstrative example is considered to highlight the benefits of this new approach.
Section snippets
BIM-based facility size estimation
The CSLP problem consists of optimizing the locations for temporary facilities, which may be approximated as one or more rectangles, on the unoccupied areas of a construction site. Most CSLP tools require the user to specify what the dimensions of each facility are, prior to performing the layout optimization. However, this approach has a significant drawback, which leads to an under-utilization of site space. Facilities are used for storing materials and equipment, or to provide a working area
Dynamic layout planning
A construction project commences in various construction stages or phases. Foundation, framing, MEP and finishing works are common examples of different phases. As a result, the requirement of facilities will also differ between phases. Facilities required in one phase may not be required in the next phase. In such cases, unnecessary facilities may be dismantled after use and the space previously occupied by them could be used to set up other facilities. In certain cases the use of a facility
Objective function formulation
After determining the facility requirements and space availability, we formulate the CSLP problem into an optimization problem. On a construction site, personnel travel from one facility to another either to perform certain activities or to transfer materials. Each such trip is assumed to incur a cost, which is directly proportional to the distance travelled and also depends on the mode of transportation. The frequency of these movements is stored in a trip frequency matrix, whereas the cost of
Optimization using genetic algorithms
The CSLP problem is considered to be ‘NP-hard’ and several research studies have attempted to arrive at solutions using heuristics [13], [14], [17], [32], [34] or mathematical optimization techniques [28]. Genetic algorithms (GA), due to its ease of implementation, is one of the most common methods of solving the site layout problem. The essence of GA lies in combining elements from two solutions of the same generation (parents) or mutating individual solutions to produce a third solution which
Demonstrative example
We tested the BIM based CSLP framework on an illustrative example of a building construction project. The project involves construction of a 12-story steel building with concrete floor slabs on a site area with some existing trees and an access road. Autodesk Revit was used to create the BIM model, which contained the material information of the building (see Fig. 8). A schedule for the construction activities was created using Microsoft Project (see Fig. 9).
The construction site was converted
Conclusions and future work
This paper presents a BIM based framework that enables automating the construction site layout planning (CSLP) process. Based on this, we developed a tool to optimize the dynamic layouts of temporary facilities on a construction site. Our framework allows quick and easy facility sizing and does not require users to manually input project specific information. Since all the calculations are based on information from BIM, changes to the design and schedule could be updated at the click of a
References (40)
- et al.
Automated multi-objective optimization system for airport site layouts
Autom. Constr.
(2011) - et al.
A hybrid CAD-based construction site layout planning system using genetic algorithms
Autom. Constr.
(2003) - et al.
Dynamic site layout planning through minimization of total potential energy
Autom. Constr.
(2013) - et al.
The time dimension in site layout planning
Autom. Constr.
(2014) - et al.
Dynamic construction site layout planning using max-min ant system
Autom. Constr.
(2010) - et al.
Optimal utilization of interior building spaces for material procurement and storage in congested construction sites
Autom. Constr.
(2013) - et al.
Multi-objective dynamic construction site layout planning in fuzzy random environment
Autom. Constr.
(2012) - et al.
Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights
Autom. Constr.
(2014) - et al.
GIS-based dynamic construction site material layout evaluation for building renovation projects
Autom. Constr.
(2012) - et al.
Automated multi-objective construction logistics optimization system
Autom. Constr.
(2014)
Extending BIM interoperability to preconstruction operations using geospatial analyses and semantic web services
Autom. Constr.
Particle bee algorithm for tower crane layout with material quantity supply and demand optimization
Autom. Constr.
BIM-based fall hazard identification and prevention in construction safety planning
Saf. Sci.
Applicability of 4D modeling for resource allocation in mega liquefied natural gas plant construction
Autom. Constr.
Application of 4D for dynamic site layout and management of construction projects
Autom. Constr.
Floor-level construction material layout planning model considering actual travel path
J. Constr. Eng. Manag.
4D dynamic management for construction planning and resource utilization
Autom. Constr.
Hybrid intelligence utilization for construction site layout
Autom. Constr.
A comparative study of different approaches for finding the shortest path on construction sites
Procedia Eng.
A fuzzy based multi-objective path planning of construction sites
Autom. Constr.
Cited by (155)
Fully integrated construction planning
2023, Automation in ConstructionBIM-based estimation of inputs for site layout planning and locating irregularly shaped facilities
2022, Automation in ConstructionCitation Excerpt :Recent studies used different algorithms, such as A* and Dijkstra [52], based on graph exploration techniques to estimate the travel distance. For example, Kumar and Cheng [15] used the A* algorithm to find the shortest path between facilities. Moreover, Abotaleb et al. [18] measured the distance by the Shortest Walk distance on Grasshopper, a visual programming language and environment.
Data-Based Reachability Analysis and Optimized Robot Positioning for Co-Design of Construction Processes
2024, 2024 IEEE/SICE International Symposium on System Integration, SII 2024Building Information Modeling and Lean Construction
2024, Building Information Modeling: Shared Modeling, Mutual Data, the New Art of Building