Facilities Construction Project Procedures Manual The: Fill & Download for Free

The main aim of O&M manual is to act as a reliable source of knowledge for the owner/ customer after the completion of the project. They will refer back to it when they need information about the operation of any equipment or ordering material. It outlines the construction of the building and all its system , as well as the procedures for the operations and maintenance of the facility. Please follow Domitos Facility Management to get more details

The Design-build method:The primary method of executing projects in the public sector in the United States (US) has traditionally been the design-tender-construction execution. The public sector has historically separated design and construction contracts. In the 1990s, the North American public sector began to experiment with the execution of design-construction projects (turnkey), which combines design and construction in a single contract. In 1997, a decision support system was established, in order to deliver a formal selection model for the execution of projects in the public sector. The model supports public sector owners to determine which projects are suitable for design-construction (turnkey) execution. This initial model by nature was static and was based on a regression analysis of 104 projects. The analysis produced a predictive model with five performance criteria: general satisfaction, administrative burden, meeting expectations, program variation, and budget variation. Since 1997, the number of design-construction projects has increased dramatically and the design-construction methods of the public sector have evolved. The original model can be improved with new data and a structure that translates into an adaptive model, while the industry continues to evolve. This document presents a formal application and the use of capabilities to complement the original static model. This model fits parameters and functions through the use of artificial intelligence, as the main engine of knowledge. This approach can be adapted to many decision support applications in the design and construction industry.Introduction and motivationPublic sector owners in the United States (US) have historically restricted themselves to the execution of design-tender-construction projects. Owners hire designers through a selection based on professional skills to design a project. Once the design is finished, the owners get the builders based on the best economic proposal. In the early 1990s, public sector owners began experimenting with the execution of design-construction projects (turnkey), in which they hired only one entity,the designer-builder, to complete the design stage and build the project. A large number of successful experiences led owners to increase the number of design-construction projects. However, many others experienced failed, as they chose projects that were not appropriate for the execution of the design-construction method.In 1997, a strategic decision support system was implemented to deliver a formal selection model for the execution of design-construction projects. The model supports public sector owners to determine which are the appropriate projects for design-construction execution, thus increasing their chances of success (Molenaar and Songer 1998). The support system resulting in the decision-making process, available on the internet, has the name of Design-Construction Selector (SDC) (Molenaar and Songer 2001). The original research analyzed 104 projects, through a retrospective case study approach, which resulted in a predictive model with five criteria: general satisfaction, administrative burden, meeting expectations, Program variations and budget variations. Owners can enter the characteristics of their project into the decision support system, available on the internet, to match the designer-builder candidates with the 104 projects in the case study. The importance of the objectives of the project may vary, through a comparison in pairs of the five criteria. The result of the model yields a score that is compared with the projects in the case study, using the combined performance criteria score for each of the performance criteria. to match the designer-builder candidates with the 104 projects in the case study. The importance of the objectives of the project may vary, through a comparison in pairs of the five criteria. The result of the model yields a score that is compared with the projects in the case study, using the combined performance criteria score for each of the performance criteria. to match the designer-builder candidates with the 104 projects in the case study. The importance of the objectives of the project may vary, through a comparison in pairs of the five criteria. The result of the model yields a score that is compared with the projects in the case study, using the combined performance criteria score for each of the performance criteria.The objective of this research is to develop a learning engine for the SDC System, in order to adapt the model, while the execution of public sector projects evolves. Since 1997, the online SDC tool has been visited by more than 12,000 people; and the tool as such, has been used in more than 200 projects, which represents over 5 billion dollars in design and construction. There is currently a considerable amount of data from new projects that can be used to improve the original static model.A review of 93 Support Systems in Decision Making (DDS) in the field of construction during the last 30 years, shows that the majority are static, with fixed parameters, functions and standards .Static models can quickly become obsolete, requiring manual adjustments to regain importance in the dynamic environment of engineering in construction and management. An improved approach to solve the problems of changes in the strategic decision environment is to develop dynamic models based on learning systems (Bastías and Molenaar 2005; Bastías 2006; Taylor and Bernstein 2009). This document presents a formal application and the use of learning capabilities to complement the original static SDC model. Through this presentation, it is expected that the general approach can be adapted to many applications in support of strategic decision making, in the design and construction industry.Background of the design-construction model in the public sectorA project execution method is a broad process in which designers, builders and various consultants provide services for the design and construction, in order to deliver a complete project to the owner. The two methods of execution in the North American public sector (US) are design-tender-construction and design-construction. The Figure 1 shows graphically the execution methods, indicating the contract and the inherent flow communication with each method.Figure 1 : Project Execution MethodsBy employing a method of executing design-tender-construction projects, an owner instructs a designer to provide the design services and then awards the construction contract separately, based on the complete construction documentation delivered by the designer. The owner is responsible for the details of the design and the construction contractor is responsible for complying with the quality of the construction documents. The Figure 1shows that the contracts place the owner between the designer and the builder in the project execution process. The linear nature of the process makes the design-tender-construction contract a major effort. The design-tender-construction contract process also places the responsibility for the accuracy of the details of the design, in the course of construction, clearly on the owner. As a result, the owner is responsible for errors or omissions in design costs, compared to the builder. There is little incentive for the builder to minimize or improve costs. Actually, the opposite effect may arise. When the design-tender-construction projects are awarded based on the best economic offer,The design-tender-construction contracts do not give the opportunity to deliver design suggestions to the general contractor, since this is selected based on the best economic offer, after the design has already been completed. Thus the owner must rely only on the designer for the estimation of costs and the review of the constructibility, during the design process. On the contrary, in the method of execution design-construction of projects, the owner retains both services, design and construction, in the same contract. The Figure 1indicates that the contractual approach is direct. The design-construction execution involves the contractor from the beginning of the design and it provides constructibility opinions inherent in the design process. The designer-builder is the legal entity that owns the design detail during construction and, as a result, is responsible to the owner for the costs, errors or omissions that occur during construction. Since the owner no longer possesses the details of the design, his relationship with the designer-builder must be based on a strong mutual degree of professional confidence. The designer-builder can literally control the execution of the project. As a result, the design-construction execution method has proven highly successful,Due to the minimum amount of design developed, at the time of the award of the contract, the designer-builder is generally selected based on the hiring of the best offer, combining cost and price. Public sector projects often use the provision of money per lump sum in the design-construction method, but it is also feasible to guarantee a maximum price.Although the design-construction model has been used for centuries, it was not until the late 1990s that the public sector began using it in the United States (US). In the course of the 80s and 90s several federal and state agencies experimented with the design-construction model for military accommodation, collective dormitories, inns (motels), warehouses, courts, mail distribution facilities, vehicle maintenance facilities, laboratories, health clinics, federal courts, and highways. (Molenaar et. To 1999). However, projects were typically executed under special legislation or legal provision, since the design-construction method was not widely accepted by public sector award laws. It was not until 1996 that the Acquisition Reform Act granted federal authorities the legal authority to contract design-construction projects (Molenaar et. Al 1999). The rapid growth and the novelty of the design-construction method in the public sector created the need for support in strategic decision-making, in order to choose the public sector projects that are appropriate for this model.Original SDC solutionThe support system in strategic decision-making identified five performance criteria (dependent variables), which are directly related to the development of the overall execution of the project. The performance criteria are obtained by evaluating 36 project characteristics (independent variables), through a questionnaire aimed at obtaining objective information about a specific project. The questionnaire is divided into four sections, as shown in Figure 2 (Molenaar and Songer 1998).Figure 2: Hierarchy of Questions in the Selection of the Design-Construction ProjectA successful definition can include many criteria. Cost, program and customer satisfaction are the main categories used to define the success of an SDC. The five performance criteria are (Molenaar and Songer 1998):General satisfaction: The general satisfaction of the owners with the project. The scale ranges from 1 (not satisfied) to 6 (better than expected).Administrative burden: The administrative burden for a potential design-construction project compared to other projects. The scale ranges from 1 (low) to 6 (high).Compliance Expectations: Meeting expectations for a potential design-construction project compared to other projects. The scale ranges from 1 (non-compliance) to 6 (better than expected).Program Variation: The variation in the project program from the time the contractor was awarded until the completion of the project.Budget Variation: This equation predicts the variation of the project budget, from the moment the contractor was awarded until the completion of the project.Each performance criterion is modeled by a multiple linear regression. Regression models for performance criteria are based on the 104 original case studies (Molenaar and Songer 1998). In order to obtain a general project score, five individual regression equations were combined, using a linear model, in which the relative values ​​for each of the performance criteria are determined solely by the user of each project. The final score is predicted as follows.The results of the general score indicate to the user the strategic support information that can be used to increase the probability of success of the individual project (Molenaar 1997; Molenaar and Songer 1998). While the user can generate different relative values ​​for the entire model, based on the individual needs of their project, the individual performance criteria equations are based only on the 104 projects originally used to create the support tool in strategic decision making SDCLearning solution:The starting point for this research focuses on the learning skills model. Adaptable models may vary over time, adjusting their parameters and functions to increase the accuracy of the result, while obtaining more information. In general, there are three types of data: input data, time factor data, results data. Given a process with N pairs of input / result data, (see Equation 2), the main objective of the adaptive model is to choose the model that meets (see Equation 3) (Harris, Hong et al., 2002). Techniques have been developed to approach this equation in the field of artificial intelligence.Artificial intelligence is a branch of computer science that has been developed with the purpose of solving learning problems. Although there are many different approaches in artificial intelligence such as evolutionary computing, fuzzy logic, genetic algorithms (to name a few), the most prolific application of artificial intelligence to learning concepts is the neural network (Jain and Martin 1998). Neural networks learn, by definition, through training and / or adaptation, depending on the learning algorithm used in each case. Previous research has studied the general applicability of neural networks in the field of construction (Moselhi, Hegazy et al., 1991),As well as more specific solutions to construction problems, such as tenders (Moselhi, Hegazy et al. 1993), estimates (Chao and Skibniewski 1995), and project adjudication (Kumaraswamy and Dissanayaka 2001). Therefore, neural networks have been combined with other technologies, such as fuzzy logic and genetic algorithms, in order to produce more sophisticated and realistic learning systems (Jain and Martin 1998; Harris, Hong et al. 2002; Cheng and Ko 2003).Figure 3: Diagram of a basic learning systemFigure 3 shows a basic diagram of the learning system. There are three main components: input, learning engine and output. This diagram constitutes the basic approach of the proposed system. It serves as a reference to create the necessary steps to identify and develop the structure and procedures of a learning system. The three components are described below.Input: The input data is the most sensitive information provided to the model. Its accuracy will directly impact the result. Any model is sensitive to input variables, so an in-depth analysis of the type and quality of information is necessary for the creation of a successful model.Learning Engine: The most complex component of any type of learning system is the learning engine. This has been designed to make changes of parameters and functions, using the information of the result. In most cases, a type of artificial intelligence is used as an engine (example: neural networks, fuzzy logic, genetic algorithms or a combination of techniques).Result: Generally the result is easy to measure and produces valuable information. The result is the information used to make the strategic decision. The results will vary from problem to problem, and due to the nature of the learning system, they will be determined by the ability to provide the system with reliable input data.Learning model for design-build project selection:Artificial neural networks are relatively rudimentary electronic models, based on the neuronal structure of the brain. The network works like a "brain" and learns from experience. This modeling brain also promises a less technical way to develop mechanized solutions.The original mathematical structure for the SDC is relatively similar to the structure presented by the neural network. The set of input data / results in the DBE can remain the same in the neural network solution, but the internal structure changes. The algorithms and functions used by the neural network are defined by the data thrown in past projects. While the mathematical structure of the original SDC linear model and neural networks are similar; Neural networks have the ability to learn using new information that a new set of input / result data can deliver. The specific learning algorithms and the type of learning defined for each neuron in the network govern this learning.The main drawback in the use of neural networks is the amount of data required to train the network. Rare to produce accurate results, with a low number of data entry points to train, it is important to use an efficient and effective distribution of this set, which is relatively subject to problems. The additional challenges of neural networks are paralysis and local minimums. Paralysis refers to the case when the relative values ​​unpredictably reach a state of paralysis, and are not adjusted during training. The local minimums refer to the case when the relative values ​​are located in a lower state than the optimum (local optimum v / s global optimum) (Jain and Martin 1998).In most cases, the neural networks are adjusted and / or trained, so that a particular input data leads to a specific objective result, as shown in Figure 4. The network adjustment is based on a comparison of the result with the objective, until the network links the objective, or until an allowed error occurs. There are two types of knowledge: supervised or unsupervised. Supervised learning requires a "teacher." The teacher can be a set of training data or an observer that classifies the performance of the network results. Either way, having a teacher is learning through reinforcement. When there is no external teacher, the system must organize itself with some internal criteria designed in the network. Another important internal characteristic of the neural network is related to its layers. A large number of layers, in most cases,It increases the accuracy of the results, but requires a greater number of data entry to train the network properly. (Hüllermeier, Renners et al. 2004; Hinton, Osindero et al., 2006)Figure 4: Diagram of an Artificial Neural NetworkSome notable advantages and disadvantages:Advantages: Neural networks learn the behavior of the system, using the input data and results. The representation of the process of human thought is good enough to solve many problems, either unsolved or insufficiently resolved with existing techniques. The networks incorporate the previous information to satisfy the following advanced solutions.Disadvantages: The biggest problem is the nature of the "black box". You have not finished understanding what is inside the black box. It is very difficult to determine the appropriate structure and layer of the network to solve the problem. Manipulating and acquiring learning and convergence parameters is a task whose difficulty is increasing. Another important disadvantage is related to the amount of data set needed to train the neural network. Depending on the number of components and layers, the neural network may require a greater volume of information: this is a factor that the construction industry must still control.The input / result data set remains identical between the original solution and the new SDC solution. A set of 25 input data are related to five results. Therefore, the neural network has 25 input nodes associated with five result nodes. A categorical type of node reflects an exclusive variable, for example type of contract. As mentioned earlier, the initial configuration was carried out using 104 projects and was validated by 18 projects in 1997. Since then, many projects have been completed providing new information to the model to improve the accuracy of the results.The most complex and tedious task of building the neural network is the selection of the number of hidden layers or nodes and the learning parameters, such as the learning norm and learning speed. There is an implicit correlation in the construction of neural networks. If the number of nodes is very small, the network loses its ability to learn. In order to design "the least complicated network, with acceptable performance", it is necessary to establish a "trial-error" procedure (Cimikowski and Shope 1996).After a thorough analysis and design of the network, a trial-error procedure was performed adding nodes to the hidden layer and a review of the improved network performance. During these experiments, the changes were monitored with a performance indicator (final score), the mean square error (MSE), the average error (MEAN) and the standard deviation error (STD). The proposed solution includes a set of five neural networks associated with each performance criterion. All networks have the same structure, with an input layer composed of three neurons and two hidden layers, where the inner layer has two neurons and the resulting layer has only one.The Figure 5 shows the design of the network for performance criterion General Satisfaction. To develop the network, a Red Neuronal ® Matlab ?? toolbox was used. The algorithm used by the network is backpropagation, with Purelin as a transfer function, Translm as a training transfer function and Train as an adaptive function. The performance function minimized the mean square error (MSE). The inner layer has a vector of 8 data points with three neurons.Figure 5: Networks for the "General Satisfaction" indicatorNeural networks must be trained to adjust relative value and propensity. The training process requires, in most cases, a considerable amount of data; However, this amount can be reduced if the data set is representative of the problem to be solved. The adaptation is that the network, previously trained, be adjusted using new information through feedback. The simulation is exactly the evaluation of the network using certain input data.The data set for the new model considers 152 projects, with the design-construction solution (SDC). In order to maintain a parallel between the original solution and this new approach, the network was trained with the same initial set of data (ie the original 104 projects). Then it was adapted with 18 projects and evaluated with 29 projects. The Table 1 shows the results of evaluation in three cases: the static solution, the initial neural network and the neural network adapted by additional data set.The Figure 6 shows five neural networks with SDC solution and represents the general topology. The result of each neural network is transformed to a scale of 1-100, and then to a linear model. The final score is calculated as an average of each performance criterion, on an adjusted scale. This calculation is done to make a direct comparison, since the relative values ​​are different for each project. As we discussed earlier, a dynamic comparison in pairs was carried out in the SDC solution, to generate the relative values ​​for each exclusive project.Figure 6: Final Topology of Neural NetworksAs shown in Table 1, the model shows a particular improvement from the regression model. As an example, a large public university invested USD 12,000,000 in the extension of an auxiliary building. The information in this project was used to analyze the performance of the current new model. The neural network indicates the prediction of success for the project and, therefore, provides more accurate consultative information. The percentage of error in the final estimate decreased from 7.1% to 0.5%, in this particular case. The result, based on the SDC solution, provides a general score based on the predictions for each of the performance measures. The result for this project showed that he was a good candidate for design-construction execution.For more details on this consultative information, see Molenaar's research (Molenaar and Songer 2001). An important aspect of the SDC solution is not only the additional accuracy, but even more relevant is how the feedback information was used to continuously improve the result. The characteristics of the owner, the project, the market and the relationship will change as the public sector evolves and becomes familiar with the design-construction execution. If the neural network model can be provided with adequate data, this model will deliver more accurate predictions and provide more applicable consultative information.In the End:This research promotes the area of ​​decision support systems in construction and management engineering, by applying a learning model to a problem originally solved with a static model. Although by nature, engineering and construction management is a very dynamic discipline, the vast majority of decision support systems are static. The SDC complies as a representative case study, since it is a typical decision system in this area.Although this document provides only one case study for a learning model, applied to engineering in construction and management, the authors see many opportunities for future applications. The SDC case study is an example of a strategic decision that involves the selection of an execution method. Similar dynamic problems are present at this strategic level, as seen in the literature. This methodology can be applied by a contractor to the selection of international projects (Han and Diekmann 2001), or to the decision of an owner to select pre-qualified contractors (Russell et. To 1990). On a strategic level, there are certain barriers such as the time required to gather the feedback results, the amount of data required for a reliable result, the number of complicated variables in the decisions, and the acceptance of the information supporting the decision by those responsible for it. However, as shown in the SDC case, these barriers can be overcome with long-term planning and consistent data collection.The application of this learning methodology can also be applied to production levels.Similar studies that can benefit from this methodology at the productive level include shared resources in linear construction (Perera 1983), optimization of multiple heavy loads, (Lin and Haas 1996), time-cost-quality correlation analysis for road construction (El-Rayes and Kandil 2005). The length of time required to gather feedback results and the number of complicated factors is less in the productive level. However, other barriers to implementation could include a shorter time structure in implementing the results and changing production systems, during operations. But these barriers are minimal compared to the potential gains that can be found.Finally, the field of engineering in construction and management will benefit from the incorporation of new learning capacities of decision support models. The use of artificial intelligence applied to learning abilities in their decision models is an appropriate form; However, other methods can also be used. The key is not to let the models remain static and use new information to increase the accuracy of future results. The SDC case demonstrates that, with proper planning, dynamic decision support systems can improve development, without creating excessively heavy

Of your current assignments, what do you consider to have required the greatest amount of effort with regard to planning/organization? How have you accomplished this assignment? How would you assess your effectiveness?Thank you for the opportunity to answer.I do many types of work - industrial and commercial / domestic design, personal counselling and advising, construction, project management, teaching, and writing ops and procedures / franchise manuals, writing poetry books, books on applied meta-physics, etc...1. What do you consider to have required the greatest amount of effort with regard to planning/organization.Learning to plan and organise is a cumulative process. Each day its gets easier as each day there is a much larger library of my own work I can draw from. I have four decades of work, which I can refer to. I use a set format / procedure for planning/organisation, which I refine for each project's requirements.2. How have you accomplished this assignment?1. Discuss the client's needs / or list my own objectives, if it one of my own projects.2. Create a list of client's expectations.3. Describe in detail how I shall fulfill those expectations.4. Plan the process in great detail.5. Work with the client to provide what they need.6. ALWAYS bear in mind, a client uses my services to design and build their dream, not as many 'designers' do - expect the client to finance their dream design.How would you assess your effectiveness?1. Complete the project to the stage required is the proof of the pudding.2. A t last count, 7 out of 10 of the current top rated restaurants in my city, of the last 36 months, operate with my designs. A number of the facilities I have designed are the most popular restaurants in the city. My designs help them grow efficiently.3. A good number of franchises use my methodology and designs for their central kitchens. All are growing and thriving in what is presently considered a ‘poor economy’.4. A constant stream of referrals for new projects.5. I provide a range of services Business Management Meta-physics - the first company in the world to do so. Applied Meta-Physics is the Science of Being and Knowing - sometimes called the Science of Making the Ideal Real. This is what I do - I guide clients through the process of making their idea(ls) - manifest.6. The companies using the services I provide and who actually carry them forward and develop their people - thrive.If you have gained some insight from reading this, please upvote and follow to share in some more unique Meta-Physical insights.