Construction Site Layout Research Articles

A Hybrid Tabu Search-Based Artificial Immune Algorithm for Construction Site Layout Optimization

Layout of temporary facilities on a construction site is essential to enhance productivity and safety. It is a complex issue due to the unique nature of construction. This problem is validated as an NP-hard and one of the challenging problems in the field of construction management. In this paper, we proposed a hybrid algorithm, named topt-aiNet, to solve the construction site layout problem by combining the aiNet algorithm with Tabu search. Experimental results showed that the proposed algorithm outperformed the stateof-the-art ones. DOI: 10.32913/rd-ict.vol2.no15.470

Charged System Search and Magnetic Charged System Search Algorithms for Construction Site Layout Planning Optimization

Construction site layout planning can be considered as an effort to place different temporary facilities in available site locations such that multiple objectives are satisfied as much as possible. With the extension of high-rise building construction and construction activities besides the lack of available spaces in construction sites, proper utilization of this resource has been highlighted because of its significant positive influences on direct cost, safety, and security of the site which consequently affects the total cost and schedule of the project. Thus the construction site layout planning is considered as one of the essential and important phases in construction projects. Site layout planning problem is an NP-Hard problem from the viewpoint of complexity. In this research, two prominent meta-heuristic algorithms, namely Charged System Search (CSS) and Magnetic Charged System Search (MCSS) are utilized to optimize the site layout planning problem. The obtained results of implementing these two algorithms for two different types of site space modeling are compared with the results of the Particle Swarm Optimization (PSO) algorithm and also those of the previous studies. The results illustrate the capability of the CSS and MCSS algorithms in solving the present problem.

A tri-objective ant colony optimization based model for planning safe construction site layout

Abstract Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a tri-objective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.

Conflict resolution-motivated strategy towards integrated construction site layout and material logistics planning: A bi-stakeholder perspective

Abstract Efficiency of construction site layout planning (CSLP) and construction material logistics planning (CMLP) is of great significance for a successful project control. Despite considerable research undertaken to separately optimize the site layout plan or material logistics plan, the interdependencies and decision conflicts of the layout planner and the logistics planner are neglected in the construction planning and design phase. The ignorance of the decision conflicts and interdependencies can lead to an overrun cost, a delayed project or a worse quality. This research proposes a decentralized methodology based on a bi-level model and a Bi-PSO algorithm, enabling the layout planner and the logistics planner to optimize the CSLP and the CMLP in an integrated model. Fuzzy random parameters are utilized to deal with uncertainty in the data collection process. By utilizing the methodology, the stakeholders are capable of adjusting the respective decisions based on the changes of others. By iteratively adjusting the CSLP and CMLP solutions, the CSLP-CMLP decision conflicts are dealt with, accordingly, the bi-stakeholder conflict resolution-motivated CSLP-CMLP strategy is planned. To demonstrate the applicability of the proposed methodology, a real case study is conducted to optimize CSLP and CMLP simultaneously. The significance of optimizing an integrated CSLP-CMLP for the layout planner, the logistics planner and the general contractor is also justified. The contributions of this research include the capability of modeling the decision conflicts in a bi-level model, the ability of generating an integrated CSLP-CMLP solution and the practicality of guiding academic researchers and practitioners to optimize construction management from a global viewpoint. The research can be extended to other fields to explore the effects of decentralized decision making on other bi-stakeholder conflict resolution cases.

A Hybrid Tabu Search-Based Artificial Immune Algorithm For Construction Site Layout Optimization

A Hybrid Tabu Search-Based Artificial Immune Algorithm For Construction Site Layout Optimization

Research Review on the Teaching Reform of the Civil Engineering Courses Based on the BIM Mode

The three aspects of the civil engineering construction site layout, project management, construction site simulation were applied to the classroom teaching of the civil engineering on the BIM technology. Let the students fully understanding and application of the BIM technology, and achieved the full unity of the theory and the practice. Through the BIM technology to manage the construction project, finally it was be applied to the teaching reform based on the results of the study process, and to improve the teaching effect.

A hybrid WOA-CBO algorithm for construction site layout planning problem

The whale optimization algorithm (WOA) is a recently developed swarm-based optimization algorithm inspired by the hunting behavior of humpback whales. This study attempts to enhance the original formulation of the WOA by hybridizing it with some concepts of the colliding bodies optimization (CBO) in order to improve solution accuracy, reliability and convergence speed. The new method, called WOA-CBO algorithm, is applied to construction site layout planning problem. To show the efficiency and performance of the WOA and WOA-CBO in construction site layout problems, three case studies are selected. First case is a discrete and equal area facility layout problem that every facility could assign to any location. Second case is an unequal area version of discrete facility layout problem with more constraints and the last case is a continuous model of construction site layouts. These cases are studied by WOA, CBO and WOA-CBO, and the results are compared with each other.

Site Layout and Construction Plan Optimization Using an Integrated Genetic Algorithm Simulation Framework

AbstractEfficiency of a planned site layout is essential for the successful completion of construction projects. Despite considerable research undertaken for optimizing construction site layouts, m...

BIM-based Code Checking for Construction Health and Safety

Rule-based Code Checking validates the design phase comparing Building Information Models against current codes and regulations translated into parametric rules. The proposed paper fits into a wider project for implementing design verification and validation within a BIM environment. The design of construction site layouts and safety plans is an essential part of an effective integrated process but it is traditionally carried out by means of error-prone and inefficient manual observation and, moreover, building designers and health and safety (H&S) coordinators still lack a collaborative working approach. The digitization of the construction site allows virtual inspections and information-based analysis of construction phases. Moreover, interoperable BIM tools allow the semi-automatic review of design compliance against normative texts, improving accuracy and reliability of the validation process. The research project aims to define an H&S BIM-based design and validation workflow, specifying the minimum level of requirements and mandatory informative content for the submission of construction site layouts and safety plans. The paper is focused on the translation into a parametric rule-set of the Italian construction sites’ H&S normative text (D.Lgs. 81/2008). A semantic analysis was used in order to translate it into computable parameters to be implemented into checking rules. Object tables have been created for each construction site element regulated by the D.Lgs. 81/2008. Based on those tables, meant as guideline for the design phase, a BIM library for the construction site has been created and a model checking tool has been used for creating rules to check and validate BIM objects and mutual relations. The customized rule-set includes legal references and information requirements specifications. Such an approach aims at including the design of construction safety plans within digital

BIM-based Applications of Metaheuristic Algorithms to Support the Decision-making Process: Uses in the Planning of Construction Site Layout

The use of Building Information Modeling (BIM) is affecting the way in which construction projects are planned, designed, executed and operated. One of the main goals of BIM is to provide, based on a 3D model, an accurate information model in a digital format to give different project participants better tools when evaluating different options to support their decisions about the project at a given phase. With this in mind, BIM provides a suitable framework to support the decision-makings process by aggregating the necessary information at the right time, and clarifying details and existing conditions; however, the different elements required to make an optimized decision need additional consideration. To address that, this paper explores the value of metaheuristic algorithms for reaching an optimized solution. The use of metaheuristic algorithms is well known in various aspects of knowledge optimization. This paper provides an overall review of the applications of metaheuristic algorithms in BIM-based optimized decisions in the construction industry and focuses on applications to the planning of construction site layout. Based on the findings from this paper, research gaps in this area have been identified and suggestions for future research to address those gaps are suggested.

Genetic Algorithm–Simulation Framework for Decision Making in Construction Site Layout Planning

AbstractSite layout planning is a complicated task in many construction projects because of the diversity of decision variables, conflicting objectives, and the variety of possible solutions. This paper describes a framework that facilitates decision making on site-layout planning problems. The framework consists of three phases: (1) functionality evaluation phase (FEP), which qualitatively evaluates using a new method; (2) cost evaluation phase (CEP), which quantitatively evaluates the goodness of the layouts using simulation; and (3) value evaluation phase (VEP), which selects the most desirable layout from both qualitative and quantitative aspects. This framework also takes advantage of heuristic optimization through genetic algorithm (GA) to search for the most qualified layouts within FEP. The primary contribution of this research is to introduce a novel method for evaluating quality of layouts, which more realistically model the closeness constraints, and consider size and location desirability in t...

Optimal construction site layout based on risk spatial variability

Abstract The construction site layout planning is a complex and important task conducted by project managers and planners. It must able to face the occurrence of potential hazards like fire and blast waves, for instance. However, minimizing risk resulting from natural or technological hazards is still a scientific challenge. In the present paper, a new methodology is developed in order to evaluate the risks within a construction site. It consists of: Modeling construction site components, for instance; electric generator, fuel storage, offices, equipment and material storages, in 2D layout. These components act as hazardous sources and potential targets at the same time, Modeling hazard interaction matrix: it shows the hazard interaction among site components and the attenuation of hazard with distance, Modeling vulnerability interaction matrix: it represents the potential weakness of whole targets to the hazard generated from each source. In the present research, the vulnerability is expressed as function of hazard intensity, Defining the utility function: it aims to afford an optimized site layout with minimum total risk in the construction site, finally Performing spatial analysis technique, utilizing space syntax principle, to realize space configurations in the construction site. As the evacuation process is considered in evaluating and visualizing the risk, the actual risk is amplified by utilizing penalty factor called mean depth. Geographic information system (GIS) is useful in visualizing the spatial variability of the risk within the site. It integrates the potential total impact of the facilities with the space configuration mean depth results. For illustration purposes, the methodology is employed in a case study consisting of several facilities acting as hazardous sources and potential targets in a 2D layout. The risk optimization considers the level of hazards at each source object, hazard attenuation and adopts conditional values for the vulnerability of the target objects. A differential evolution algorithm is adopted to minimize the global risk within the site. The results showed that the proposed methodology is efficient, due to its capability of generating site layout with safer work environment. This in turn leads to minimize work accidents, serious injuries and victims. In addition, the model is capable of highlighting the highest risk areas within a construction site.

Bilevel Construction Site Layout Optimization Based on Hazardous-Material Transportation

AbstractConstruction site layout optimization (CSLO) involves many important issues vital for the success of construction projects. One of the most important is the hazardous-material transportation (HT) problem. This paper concurrently considers CSLO and HT, namely the CSLO-HT problem, and proposes a bilevel multiobjective decision-making model (BMDMM). In this model, the upper-level decision-maker is the project manager, who aims to minimize site layout costs and economic losses from potential HT accidents. The lower-level decision-maker is the carrier, to whom the HT work is subcontracted, and whose goal is to reduce transportation costs. To solve the proposed bi-level multiobjective model, a fuzzy random simulation–based bilevel multiobjective genetic algorithm (frs-BLMOGA) is proposed. The approach is then applied to a hydropower construction project to illustrate the performance of the proposed methodology. The results and further analyses of the methodology prove that both the project manager and t...

Multi-objective Construction Site Layout Planning Using Genetic Algorithms

Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety in construction sites. This task usually consists of identifying the temporary facilities needed to support construction operations, determining their size and shape, and optimally positioning them in the unoccupied areas within the site boundaries. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the number of facilities and constraints increases. The existing literature includes a variety of analytical, heuristic, and meta-heuristic techniques for solving the problem but existing studies usually examine a small number of facilities and focus on travel distance minimization, ignoring generally cost related or other decision parameters. The objective of this study is to develop feasible and efficient site layout solutions in a realistic representation scheme taking into consideration not only the total distance traveled but also cost and safety parameters as well. A multi-objective optimization model is developed aiming at minimizing a generalized cost function which results from the construction cost of a facility placed at alternative locations, the transportation cost among locations, and any safety concern in the form of preferred proximity or remoteness of particular facilities to other facilities or work areas. The development integrates the required robust search objective with the optimization capabilities of the genetic algorithms (GAs). The model has been tested on several test cases and the results of a comparative study with existing methods from the literature are presented. The evaluation indicates that the proposed model provides effective and rational solutions, in response to decision parameters and problem constraints, and that it results in more robust layout planning than previous methods both qualitatively and quantitatively.

GA optimization model for solving tower crane location problem in construction sites

Tower crane is increasingly becoming one of the key components of temporary site layout facilities in most construction projects. Determining the location of tower crane is an essential task of layout planning, which is also the central focus of this study. The optimization of tower crane location depends on many interrelated factors, including site constraints, shape and size of the buildings, type and quantity of required materials, crane configurations, crane type, and construction site layout. These factors vary from one project to another, resulting to complicated site layout strategies and approaches. This fact makes the crane location problem impractical to be solved depending on experience of practitioners only which was gained by assuming and through trial and error.This paper aimed at developing an optimization model to solve tower crane location problem in construction sites. The objective was to minimize the total transportation time. Genetic Algorithms (GA) optimization technique is utilized to solve the problem. A numerical example is presented to test and validate the results obtained by the model.

Bilevel and multi-objective dynamic construction site layout and security planning

Abstract The joint problem of dynamic construction site layout and security planning is fundamental to any construction project. However, it is still largely unexplored. Few research studies investigated the implementation of security measures during the construction phase. This paper proposes a bilevel multi-objective model for the dynamic construction site layout and security planning problem. Specifically, the upper-level programming denotes that the project manager must first choose the construction site layout and security strategies to minimize the layout costs and consequences of a potential attack. The lower-level programming denotes that the attacker will destroy a subset of the facilities to inflict the maximum economic consequence on the construction facilities system. Thereafter, a bilevel multi-objective Particle Swarm Optimization Algorithm (MOBLPSO) is designed to solve this model. Finally, the approach is carried out in the Xiajiang hydropower large-scale construction project to illustrate the effectiveness of the proposed model and algorithm.

Multi-Objective Optimal Construction Site Layout Planning with Maximize Approach of Safety and Environment(Case Study: Mahestan Residential-Commercial complex)

Abstract. Construction Site layout optimization is one of concern in recent years In the field of construction management. Site layout planning is an important step in building a development project is considered if a suitable design and optimization will have a large impact on efficiency and save resources. In previous research various approaches According to the designers look To solve this problem there The approaches Commensurate with the nature of the projects Are different. These research efforts as a model of multi-objective optimization Sort workshop for development aims at increasing safety and environmental aspects And also provide a lower cost. To solve the problem Sort Multi-Objective Particle Swarm Optimization algorithm As a meta-heuristic algorithms Used. Also in this study, In order to achieve a real model, A construction project in the city of Tehran. Put the study. In this model, The possibility of embedding the entrance and exit Facilities and buildings is considered.

An Improved Ant Colony Optimization Algorithm for Construction Site Layout Problems

Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many domains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings. In fact, finding appropriate settings of the algorithm’s parameters is considered to be a nontrivial task and is usually set manually to values that are known to give reasonable performance. In this paper, Ant Colony Optimization with Parametric Analysis (ACO-PA) is developed to overcome this drawback. The main feature of the ACO-PA is the ability of deciding the appropriate parameter values within the predefined parameter variations. Besides, a new approach which enables the pheromone information value to be proportional to the heuristic information value is introduced. The effectiveness of the proposed algorithm is investigated through the application of the algorithm to the construction site layout problems taken from the state-of-art. Results show that the ACO-PA can reduce transportation cost up to 16.8% compared to the site layouts generated by Genetic Algorithms and basic ACO. Moreover, the effects of parameter settings on the generated solutions are investigated.

Particle bee algorithm for tower crane layout with material quantity supply and demand optimization

Abstract The tower crane layout (TCL) problem, a typical construction site layout (CSL) problem, is currently used in a wide range of construction projects and site conditions. The tower crane is a key facility used in the vertical and horizontal transportation of materials, particularly heavy prefabrication units such as steel beams, ready-mixed concrete, prefabricated elements, and large-panel formwork. Matching the location of tower cranes to material supply and engineering demands is a combinatorial optimization issue within the TCL problem that is difficult to resolve. Swarm intelligence (SI) is a popular artificial intelligence technique that is used widely to resolve complex optimization problems. Various SI-based algorithms have been developed that emulate the collective behavior of animals such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This study applies the particle bee algorithm (PBA), a hybrid swarm algorithm that integrates the respective advantages of honey bee and bird swarms, to the TCL problem. The performances of PBA, BA, and PSO are compared in terms of their effectiveness in resolving a practical TCL problem in construction engineering. Results show that the PBA performs better than both the BA and PSO algorithms.

Construction site layout planning using multi-objective artificial bee colony algorithm with Levy flights

Abstract Construction site layout planning has been recognized as a critical step in construction planning. The basic function of this process is to find the best arrangement of the temporary facilities according to multiple objectives that may conflict with each other and subjected to logical and resource constraints. The formulation of the construction site layout planning problem as an optimization problem turns out to be a nonlinear programming problem where there are conflicting multi-objectives to be achieved. It is shown that the swarm intelligence based meta-heuristic algorithms are quite powerful in obtaining the solution of such hard to solve type of optimization problems. In this study a multi objective artificial bee colony (MOABC) via Levy flights algorithm is proposed to determine the optimum construction site layout. The model is intended to optimize the dynamic layout of unequal-area under two objective functions. The performance of MOABC with Levy flights is demonstrated on a real benchmark construction engineering of construction site layout planning problem and the optimum solution obtained is compared with the one determined by the ant colony algorithm.