TUGraz OPEN Library

Radchenko, Artem

Petrochenko, Marina

Losses Management Process On The Pre-Design Stage Of Construction

THE LOSSES MANAGEMENT MODEL  
The successful management of losses while the production of construction works is impossible 
without an adequate model, describing the whole process. The main purpose of its development 
is to obtain a clear sequence of decision-makers actions in determining the degree of influence of 
the losses on production processes and identify those whose impact is critical and must be re-
duced or eliminated altogether. In order to achieve this goal it is necessary: to understand the es-
sence of the arising losses at construction site (the probability of their occurrence, the impact on 
the production processes); to develop the most effective ways of reducing the impact of each of 
them to an acceptable level or completely eliminate them; to implement interventions for the 
processing of losses according to the selected method. Therefore, the process of managing losses 
is a set of consistent targeted actions that focus on optimization of the construction process in the 
framework of maintaining the planned budget, reducing the duration of the works and along with 
the quality. 
On the basis of the tools of risk management the following process was adopted of managing 
losses on a construction site that includes the main stages: 
1) the definition of the situation on the construction site and development of plan, setting 
goals and objectives of the management of losses; 
2) the analysis and documenting of the processes during the execution of construction works; 
3) the detection and identification of losses, their distribution into 7 major groups according to 
the methodology of lean construction; 
4) the assessment of the impact level of losses on the works production according to the anal-
ysis of the opinions of experienced experts; 
5) processing of obtained results, the verification and development of responses on the most 
influencing losses, the calculation of the losses impact on production processes; 
6) monitoring and control of the activities performed and changes. 
At the first stage while the definition of situation and development of the management plan 
the clear structure of the process should be established, to describe the process of losses identifi-
cation, assessment and processing, as well as how to control these operations. It is also need to 
know the input data of management process, i.e. the information set out in design and project 
documentation, calendar plan of construction, the scope of work, the composition of the teams 
The procedure of losses management on the pre-design 
construction stage  
A.E. Radchenko 
Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation 
M. V. Petrochenko 
Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russian Federation 
ABSRACT: This research article is aimed to assist for decision makers to make the first steps in 
loss management process on the pre-design stage of construction. The implementation of risk 
management instruments can help to assess the impact of each loss on the buildings erection and 
the possibilities to their elimination. The main purpose of such procedure is to improve the activ-
ities of the construction company, update efficiency and quality characteristics, increase produc-
tivity and reduce costs. Article briefly describes stages of loss management in lean construction. 
for the implementation of the main types of works, their description, the planned costs for con-
struction, the planned timing of construction of the building and the assigned responsible man-
agers who will be involved in the work. Based on this data, the project management staff should 
examine all construction processes which are planned from the normative point of view (the 
scope of work set forth in the construction rules and norms) and from the real, practical view, 
that is to determine the sequence of all possible operations on the construction of building "in 
nature". This stage should be based on their own experience and observations over the construc-
tion of similar facilities. The obtained data should organize and conduct their distribution by 
types of constructed structures. 
The next stage in the process of loss management is the identification of losses, i.e. the identi-
fication of the operations having a negative impact on the course of production processes. The 
results of the analysis form the list of losses, which increase costs and construction time and fur-
ther grouping by 7 types in accordance with the lean management methodology (Liker, 2013; 
Ballard, 2003; Koskela, 2000; Walbridge Aldinger Co., 2000; Womack, 2000). The result of 
this work will be the final list of identified losses during construction and installation works.  
Next stage is the direct assessment of the losses impact. For this purpose it is necessary to col-
lect data about the causes and consequences of the each of the identified losses occurrence, to 
determine which techniques should be used in assessment of their impact, to carry out operations 
of the assessment and to receive the results. The most applicable is the method of expert assess-
ment, as the simplest and easiest (Sheremet, 2011). One of the important characteristics in this 
method is the criteria of competence that can be defined considering the following qualities: the 
work experience; the level and profile of education; the profile of work performed; the level of 
solved problems; the number and level of projects in which the expert participated (Kravchenko, 
2010). The assessment of the losses impact is made on the basis of 3 indicators: the probability 
of occurrence of each loss, the impact on cost and the influence on time of construction 
(Grebneva, 2012; Fedoseev, 2012; Romanovich, 2015). The data is processed using the methods 
of mathematical statistics and econometrics. 
During the processing phase the ranking of losses is performed according to the degree of 
their influence on the construction works progress according to the specified criteria. Thus, the 
ranked list of losses is obtained. Then the results are checked, and calculated integral indicator 
(coefficient) of the impact of losses on the construction progress. There are 3 main methods of 
its definition: additive, multiplicative and additive-multiplicative (Fedotov, 2006; Kaliszewski, 
2016; Štreimikienėa, 2016). The significant drawback of additive and multiplicative transfor-
mations is the existence of unlimited opportunities of compensation (Anfilatov, 2006; Klahna, 
2016). The cumulative coefficient of losses impact can be calculated by the formula 2.1: 
j
n
j j
KKcum   1
 (2.1) 
where Kj = the value of the j-th integral coefficient of each of the identified losses, σi = the 
weighting factor of i-th loss index.(Beskorovayniy, 2005; Soboleva, 2012; Bolotin, 2010) 
The integrated coefficient (indicator) of losses impact on construction site will look like the 
following: 
z
cum
K
loss
K   (2.2) 
where z = the multiplication of the maximum (minimum) value applied by each expert, respec-
tively, for the probability of loss and impact level. 
The maximum (minimum) value of indicator Kcum is equal the maximum (minimum) possible 
value of loss index Rc,t: 
z
tc
R
loss
K
minmax/
,minmax/
  (2.3) 
When the value of integrated losses impact indicator is closer to 0, the level of losses impact 
on the construction site is lower and the efficiency of operations and productivity is higher. The 
approach to the value of 1 gives the decision maker a reason to concern measures of losses re-
duction and increase efficiency of production work. 
By results of this stage the decision-maker receives the output data - the list of losses, segre-
gated by the level of impact on three main groups: loss with the high level impact, medium and 
low. The first two groups of losses require urgent measures of reducing and elimination. The 
losses of the third group can be removed by the development of organizational procedures. 
Measures to reduce the impact of losses or their elimination are developed specifically for each 
type of operations (Adler, 2009; Mazur, 2010; Maslov, 2010). If measures have not given ex-
pected result, then the decision on development of plans for elimination of the loss consequences 
or abandon the changes. 
The process of losses management on a construction site is an iterative process. After making 
the changes, new losses can occur or the degree of influence of the existing ones can increase. 
Therefore, it is overwhelmed with operations on monitoring, documenting and control of ongo-
ing changes. The main stages of the process are monitored and checked for compliance with ap-
plicable methods. It should be noted that monitoring provides more than basic role, as the docu-
menting, though is one of the key processes in managing losses. However, without a process of 
monitoring and documenting the implementation of effective system loss management is impos-
sible. 
The model of losses control in the works production on the construction site with its major 
stages is shown on the Figure 1.  
 
Figure 1. The principal scheme of losses management model.  
CONCLUSIONS 
The presented procedure shows the methodology of losses management in the works production 
which includes all necessary steps. This new procedure presents new approaches in lean con-
struction: the assessment of experts’ competence level, method of experts’ assessment, the 
method for processing data of the survey; the calculation of integrated indicator of losses impact, 
which helps to assess the effectiveness of a construction project from the losses point of view; 
the methodology of checking the calculations by constructing the matrix (map) of losses, meth-
ods to reduce the impact level and eliminate losses were proposed, as well as monitoring of 
measures.It should be noted that the finished system of loss management is individual for each 
project. The measures to reduce and eliminate losses will be developed more carefully and de-
tailed and the probability of goals successfully achieving will be higher. 
REFERENCIES 
Adler ,Yu., Shechepetova, S. 2009. Process description the business foundations for the system of 
economy of quality. Standards and quality No. 2.: 66-69.  
Anfilatov, V.S. 2006. The system analysis in management. Moscow: Finances and statistics. 
Ballard, G., Howell A.G. 2003. Lean project management. Building Research and Information. Vol. 
31. No. 2: 119-133. 
Belokorovin, E.A., Maslov, D.V. 2010. Small business: ways of development. Arkhangelsk: DMK 
Press. 
Beskorovayniy, V.V., Trofimeko, I.V. 2005. Parametric identification of multiplicative models for mul-
ti-factor decision choice. Proceedings of the Kharkov University of Air Forces named after Ivan Kozhed-
ub conference. No. 5 (5): 74-78. 
Bolotin S.A., Dadar A.Kh.2010.  Оценка погрешности в аддитивном показателе качества кален-
дарного плана строительства The inaccuracy estimation of the additive quality indicator for the schedule 
of construction. Bulletin of the Tyva state University. No.3: 13-16. 
Fedoseev, V. V. 2012. Economic-mathematical methods and applied models. Мoscow: YUNITY. 
Fedotov, Yu.V. 2006. Methods of constructing summary evaluations of complex production systems ef-
fectiveness. Scientific reports No. 25(R). Saint-Petersburg: Grade school of management SPbSU. 
Grebneva, O.A. 2012. The theory of decision making. Irkustk: IrSTU.  
Kaliszewski, I., Podkopaev, D. 2016. Simple additive weighting - A metamodel for multiple criteria de-
cision analysis methods. Expert Systems with Applications.Vol. 54: 155–161 
Koskela, L.. 2000. An exploration towards a production theory and its application to construction, 
PhD Dissertation.. Finland: VTT Building Technology, Espoo. 
Kravchenko, T.K. 2010. Expert evaluation in the decision-making process. Actual problems of humani-
tarian and natural Sciences. No. 3: 88-90. Moscow: Moscow state university. 
Liker, J. 2013. Dao Toyota: 14 management principles of the world leading company. Мoscow: Litera. 
Mazur, I.I., Shapiro, V.D. 2010. Quality management. Moscow: High school. 
Medineckienea, M., Zavadskasb, E.K. Björka, F., Turskisb, Z. 2015. Multi-criteria decision-making sys-
tem for sustainable building assessment/certification. Archives of Civil and Mechanical Engineering. Vol. 
15, Issue 1: 11-18.  
Romanovich, M.A. 2015. The increasing of organizational and technological reliability of monolithic 
housing construction on the basis of modeling the parameters of time schedule, PhD Dissertation. Saint-
Petersburg state university of architecture and civil engineering. Saint-Petersburg: SPbSUACE. 
Sheremet, V. V. 1998. Investment management: a guidebook for professionals and entrepreneurs. 
Moscow: High school. 
Soboleva E.V. 2012. Modification of the criteria for generalized utility in identification problems of 
multicriterion choice. System Research & Information Technologies. No.3: 58-65. 
Štreimikienėa, D., Šliogerienėb, J., Turskisb, Z. 2016. Multi-criteria analysis of electricity generation 
technologies in Lithuania. Renewable Energy. Vol.85: 148–156. 
Voronova, S.P. 2003. The development of methods to assess investment and construction risks on the 
stages of the property life cycle. PhD Dissertation. Saint-Petersburg: Saint-Petersburg state university of 
architecture and civil engineering. 
Walbridge Aldinger Co. 2000. Lean Fundamentals. Internal company document. Detroit: Walbridge 
Aldinger Co. 
Womack, P. J., Jones, T. D., 1996. Lean thinking: banish waste and create wealth in your corporation. 
New York: Simon and Schuster. 


The procedure of losses management on the pre-design construction stage

Bazarova, Alina

Berkes, István

Horváth, Lajos

On the Extremal Theory of Continued Fractions

Scerbakov, Nikolai

 
 
 
 
 
 
 
 
 
AN ONLINE LABORATORY IN WEB BASED 
TRAINING 
 
N. Andreeva*, N. Scerbakov**, A.Schukin* 
 
* Institute of Computer Science and Technology 
Peter the Great Saint-Petersburg Polytechnic University 
St.Petersburg, Russia 
 
**IICM, Graz University of Technology, 
Graz, Austria 
E-mail: nsherbak@iicm.tugraz.at 
 
ABSTRACT 
. 
WEB based learning today is being widely used in almost every educational 
facility. In the last years all conceivable use cases that need to be carried out in  
such teaching mode have been defined and the features of a modern educational 
web site needs to offer are very clear. One of the most important aspects of such 
distant learning is a verification of students' progress with the course materials. 
In this paper we analyze a number of practical scenarios for checking students' 
knowledge and progress with course materials. More specifically, we discuss so-
called "course quiz" and "course diary" paradigms. Course quiz is an 
automatically selected list of predefined questions that must be answered by a 
particular student within a predefined time slot. If we speak about web based 
courses on databases, a typical quiz question might be: "consider the following 
database  schema and define a query ..." . 
Course diary is a list of tasks that are supposed to be accomplished by students 
throughout the course. Definition of tasks may significantly vary, but generally 
each task requires performing  a certain element of a database application design, 
for example, developing a database schema, proving that database schema is in 
particular normal form, implementing a database transaction, etc. 
We discuss a particular implementation of so-called web laboratory to check 
students knowledge on databases. We define such a web laboratory as a cloud 
service - set of resources and services offered through the Internet. Simply stated, 
the web laboratory is an Internet server with a DataBase Management System 
(DBMS) installation, and a number of WEB service entries for integrating the 
laboratory into other training components. The laboratory provides a possibility to 
build and use database applications in remote mode via a number of web services. 
In other words, the laboratory delivers special services for building database 
applications from a particular data center, the services can be used by other web 
client sites located throughout the world. The communication between the cloud 
server and clients is carried out using SOAP web services. 
Our goal in this research work is to describe the architecture, and specify web 
services necessary for actual usage of the laboratory. 
 
Course quiz 
 
Course quiz is an automatically selected list of predefined questions that must be 
answered by a particular student within a predefined time slot and possibly under 
monitoring by the teacher or course tutor. Course quizzes are evaluated 
automatically by the system or manually by the teacher and tutors. Students are 
getting examination points as a result of such evaluation (see Fig.1). Quizzes are 
used as a part of final course examination or as a precondition for such 
examination.  
Normally, there are two more or less independent tools that are involved in a quiz 
implementation. We distinguish a quiz authoring tool and run-time quiz 
environment. 
A quiz authoring tool supports: 
 definition of individual questions; 
 definition of a selected type of user input; 
 definition of templates for automatic answer evaluation; 
 definition of a pool of questions where individual questions are combined 
in a special way to provide random selection of questions for each student. 
  
Fig.1 A question from a course quiz 
 
Definition of individual questions is almost identical to authoring small 
multimedia documents, we can simply see the process as an HTML authoring (see 
Fig.2). 
 
 
 
Fig.2 Authoring a course quiz 
 
Selected type of user input is normally selected from a list of all supported HTML 
input elements such as text area, text field, select, checkboxes, radio buttons. As 
soon as the type of user input is selected, some additional parameters may be 
defined. For example, a teacher may select a radio button input element, define a 
number of alternatives and a legend (brief text) for each alternative. 
Run-time quiz environment is the second component of an online quiz 
application. This component is responsible (see Fig.1) for: 
 selecting a set of questions for a particular student; 
 visualizing the questions; 
 gathering and evaluating user answers; 
 syndicating students results and exporting the results in a form suitable for 
a teacher. 
 
Evaluating quiz answers. 
Automatic evaluation of user answers is one of the most desirable features of any 
e-learning platform. The algorithm of automatic evaluation essentially depends on 
a selected type of user input. The algorithm may be very simple if the answer is 
expected in a form of checkboxes or radio buttons. In this case, the teacher simply 
"inputs" a correct answer to the system, and the system compares two strings - the 
teacher's answer and a particular student's answer. In case of numeric input, the 
task is a bit more complex - teacher defines upper and low limits for the input 
value, and the system checks that the provided value matches the interval. In case 
of short textual inputs a so-called regular expressions may be used to evaluate 
user answers in many cases. Obviously, not a single of the above mentioned 
methods, can be used to evaluate user answers containing fragments of source text 
in general, and SQL queries in particular. 
It seems that the only way of automatic evaluation of such fragments is execution 
of sources using a relevant software environment. Thus, if we speak about 
programming in C++, there must be a C++ compiler installed, additionally there 
must be a number of test inputs (test cases) to run the compiled module. 
In the case of database assignments, the situation is even more complex. Any 
query definition (SQL) can be executed if: 
 an instance of DBMS is installed on the server; 
 a special database (repository of data plus database schema) was 
previously created; 
 a test database content (a set of test table rows) was added to  the database. 
On first glance, automatic evaluation of SQL queries can be done via simple 
comparison of output of a particular SQL query and a predefined correct output 
provided by a teacher. In reality, the situation is not so straight forward. In SQL, 
there might be a number of different query definitions that produce identical 
results.  
To distinguish different questions, every question gets a unique ID that is used to 
attach further parameters to the question. To implement an automatic evaluation 
of answers, the following parameters must be defined for each question. 
 a database schema and test content, this definition may be shared by a 
number or even all questions in a particular question pool;  
 maximum number of points that can be achieved; 
 a definition of the SQL fragments that is expected as a correct result. 
As a student formulates an answer (provides a source query code), the text is 
automatically submitted to the laboratory via a web-service, executed and 
automatically evaluated using the following algorithm: 
 if the source SQL fragment cannot be run by the DBMS, get an error 
message from the DBMS, and return 0 points + error message. 
 if the source SQL fragment returns a result from the database: 
o if the result does not match the predefined correct result, return 0 
points + invalid result; 
o if the result match the predefined correct result,  
 calculate a distance between the SQL fragment and a 
correct query definition using Levenshtein Metric,  
 normalize the distance for 0 - 100 percents, where 100% 
means that the two query definitions are absolutely 
identical, and 0% means that the strings have nothing in 
common,  
 return a rounded value of [max.number of 
points]*[Levenshtein Distance]/100; 
 
Course diary 
 
To understand the course diary paradigm, just imagine that a student wants to 
record the progress with a particular course in a form of brief weekly text chunks 
published regularly (say, once a week). Imagine further that a teacher prescribes 
to students dates when and in which form such diary entries must be done. For 
example, a teacher may request students to answer a couple of questions at the 
end of each teaching week or after finishing a certain topic. Moreover, the teacher 
may request students to make diary entries in a form of files that must be 
produced as home works  and  uploaded to the server. Thus, we can say that a 
teacher defines a number of tasks that must be fulfilled by students throughout the 
course. Speaking about a "database" course, we can see that the tasks may 
significantly vary, but generally each task requires performing a certain element 
of a database application design, for example, developing a database schema, 
proving that database schema is in particular normal form, implementing a 
database transaction, etc. 
 
 
 
Fig.3 Course diary 
Evaluating course diary. 
Evaluating course diary is a very tedious and time-consuming teacher task. The 
teacher must download 100s of files on a local computer, execute them, evaluate 
sources and results, etc. At the same time, automatic evaluation of such diary 
entries does not seem to be feasible. To help teachers in performing their duties 
we offer a concept of automatic generation of a report for evaluation. Simply 
stated, the teacher evaluates not a student's diary entry that may consist of a 
number of files of different formats and meanings, but a so-called evaluation 
report that automatically build by the system, and consists of texts taken from 
original files, results of compiling/running original files, quantitative 
characteristics of original files - size, number of works, distance to a template, etc. 
To produce an evaluation report a special work-flow script must be defined for 
each diary entry. The work-flow script is a list of specific actions that are selected 
from a set of predefined actions. The actions are performed on files submitted by 
students. The actions might be: 
 unzipping of a submitted archive; 
 sending a particular file to an online laboratory via a web service and 
including results into the report; 
 searching for a particular fragment in an original file and inserting it into 
the report; 
 calculating a number of words in the original file and inserting the result 
into the report; 
 calculating a distance between an original file and a template file and 
inserting the result into the report; 
 calculating a distance between a result received from the online laboratory, 
and a predefined result and inserting the  result into the report. 
Thus, as a student uploads a new diary entry, an evaluation report is automatically 
generated using a predefined work-flow script. The teacher can simply access the 
evaluation report that contains all the information necessary for evaluation of the 
entry. 
The method of generating evaluation reports significantly simplified the teachers 
work on evaluation of course diaries and gave a positive effect on student 
satisfaction with the evaluation (see below). 
 
Fig.4 Automatically generated report on a student's diary entry. 
 
Conclusion 
 
The online laboratory was practically implemented and actively used for the 
"database" courses at the Graz University for Technology, Graz, Austria in the 
years 2015 and 2016. More specifically, a quiz on databases (10 questions) was 
done by 350 - 400 students each year. The quiz consists of 6 multi-choice 
questions, and 4 free-text SQL questions. In former times, multi-choice questions 
were automatically evaluated by the system, free-text questions were manually 
evaluated by teachers and tutors. The average work load for theevaluation of a 
single questionnaire was assessed as about 21 minutes. For 350 students it was 
about 130 working hours. Since the SQL queries were mainly evaluated manually, 
there was a high number of students' requests for additional inspection of their 
answers and results. About 30 students asked for additional inspections each year, 
and unfortunately, in 10% of cases of additional inspection, the manual evaluation 
needed to be corrected. With the introduction of the online laboratory, the work 
load required for evaluating the quizzes, dramatically went down, thus, we needed 
an average of two minutes to evaluate a particular course quiz, or about 12 
working hours altogether. What is perhaps even more important, there were no 
requests for additional inspection of evaluations. Students saw the results of 
practical execution of their queries, definition of correct SQL queries, and could 
understand easily why they lost points for a particular wrong answer. A very 
similar picture can be reported in comparison of manual and semi-automatic 
evaluation of course diaries.  
 
REFERENCES: 
 
[1] Bolliger, DU., Wasilik, O.,"Factors influencing faculty satisfaction with 
online teaching and learning in higher education",Distance Education 30 (1), 
103-116 (2014) 
[2] Martín-Blas, T.,The role of new technologies in the learning process: Moodle 
as a teaching tool in Physics, A Serrano-Fernández,Computers & Education 
52 (1), 35-44 164 (2009) 
[3] Liu, SH., Liao, HL., Pratt, JA.,Impact of media richness and flow on e-
learning technology acceptance,Computers & Education 52 (3), 599-607
 162 (2009) 
[4] Ozkan, S., Kosele, R., Multi-dimensional students’ evaluation of e-learning 
systems in the higher education context: An empirical investigation, 
Computers & Education 53 (4), 1285-1296 145 2009 
[5] Lonn, S., Teasley, SD.,Saving time or innovating practice: Investigating 
perceptions and uses of Learning Management Systems, Computers & 
Education 53 (3), 686-694 145 2009 
[6] Dietinger T. & Maurer H. "GENTLE – General Network Training and 
Learning Environment", Proc. of ED-MEDIA98 / ED-TELECOM 98, 
Freiburg, pp. 274–280 (1998). 
[7] Ebner, M., Scerbakov, N., Maurer, H. "New Features for eLearning in Higher 
Education for Civil Engineering", Journal of Universal Science and 
Technology of Learning, Vol. 1, No. 1, pp. 93 – 106 (2006) 
[8] Ebner, M., Scerbakov, N., Tsang, P., Holzinger, A. (2011). EduPunks and 
Learning Management Systems – Conflict or Chance? - in: Proceedings of 
International Conference on Hybrid Learning IHCL 2011 Springer Lecture 
Notes in Computer Sciences LNCS 6837. (2011), S. 224 – 238 
[9] Scerbakov, A., Ebner, M., Scerbakov, N. (2015). Using Cloud Services in a 
Modern Learning Management System. Journal of Computing and 
Information Technology 03/2015; 23(1):75-86. 
[10] N.Scerbakov, TU Graz Teach-Center, Aviable: 
http://tugtc.tugraz.at/wbtmaster/welcome.html [13.05.2015] 


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Losses Management Process On The Pre-Design Stage Of Construction

The procedure of losses management on the pre-design construction stage

On the Extremal Theory of Continued Fractions

An Online Laboratory in WEB Based Training

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