productivity appraisal of in-situ concreting operations in

International Journal of Architecture, Engineering and ConstructionVol 4, No 1, March 2015, 26-39

Productivity Appraisal of In-Situ Concreting Operationsin Nigeria

Olatunde Olaoluwa∗ and David Abiodun Adesanya

Department of Building, Obafemi Awolowo University, Ile-Ife, Nigeria

Abstract: Despite massive construction projects across Nigeria, it is observed that productivity remainswell below that of many developed countries. The purpose of this study is to identify systematically theconstruction resource factors that influence labor productivity of in-situ concrete placement so as to build amodel that can evaluate the significance of these factors. The model can then be used by project managersto improve productivity and level of performance.Several resource factors identified through literature reviewwere tested for their impact on mean productivity rates using ANOVA and Regression analysis and the finalmodel was built through a statistical analysis conducted on the chosen factors.The results show a strong linearrelationship between the factors and productivity and that there are significant productivity rate variations forplacement/transportation method, type of pour, pour size and number of operatives. Benchmarks of concreteplacement productivity were produced for measuring progress in productivity over time and making comparisonwith other cities. It is recommended that contractors seeking to improve productivity should adopt the mostproductive approaches of employing pump and crane while using the derived model to develop a strategy forincreasing labor productivity.

Keywords: Labor productivity, in-situ concreting, regression analysis, benchmark

DOI: 10.7492/IJAEC.2015.004

1 INTRODUCTION

Construction is the world’s largest and most challeng-ing industry, a key sector of national economies aroundthe world. Construction traditionally contributes toa country’s total employment figures and the nation’srevenue as a whole (Attar et al. 2012). In the U.S.,construction accounts for 14% of the gross nationalproduct (GNP) and about 8% of total employment(Thieblot 2002). Ameh and Odusami (2002) also sub-mit that the output of the construction industry isabout 3-8% of the Gross Domestic Product (GDP) inmost countries, while Olubodun (1985) and Fagbenleet al. (2011) report that the construction industry hasbeen the largest Nigerian industry, employing a goodproportion of the work force and controlling over 50%of GNP.Productivity is a vital consideration for companies

in the construction industry in terms of survival orgrowth. As the industry’s contribution to the GDPis significant, improvement in productivity has a directimpact on all other industries as well as on the na-tional economy (Duncan 2002; Durdyev et al. 2013).According to Durdyev et al. (2013), 10% escalation

in construction productivity would annually save upto £1 billion. Productivity furthermore affects overallperformance of small, medium, or large organizationswhile human resource plays a strategic role in affect-ing productivity, making it superior in the industrialcompetition (Attar et al. 2012).Productivity has been an issue of contention in the

construction industry for the past 25 years as expertspersistently examine the assumption that it lags behindproductivity in other industries (Jang et al. 2011). La-bor productivity is more important especially in the de-veloping countries like Nigeria because labor-intensiveproduction is still widely employed in most places andmuch of the building work is still carried out manual-ly (Kazaz and Ulubeyli 2004; Alinaitwe 2006). Hannaet al. (1999) posit that labor represents the most sig-nificant risk to contractors while Tah and Carr (2001)submit that labor productivity is one of the most im-portant risks in construction projects. According toJang et al. (2011), the construction industry is labor-intensive and relies heavily on the skills of its workforcewhich is the industry’s most valuable asset, accountingat the very least for over a quarter of the total projectcost. Workforce can significantly influence cost, sched-

*Corresponding author. Email: [email protected]

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http://dx.doi.org/10.7492/IJAEC.2015.004

mailto:[email protected]

Olaoluwa and Adesanya/International Journal of Architecture, Engineering and Construction 4 (2015) 26-39

ule, and quality of a construction project, owing to itsvolatile nature while labor is regarded as an importantresource because it links all the other resources, name-ly, materials, plant/equipment, and finance, in orderto produce various construction products (Han et al.2008; Fagbenle et al. 2011). All the advantages sup-plied towards productivity growth by equipment andmaterial control can only be obtained with effectiveand optimum use of human resources, making labora most important factor in production (Kazaz and U-lubeyli 2004).Various authors (Yates 1993; Odeh and Battaineh

2002; Assaf et al. 1995; Kaming et al. 1997; Chan andKumaraswamy 2001) have shown that the constructionindustries in many developed and developing countriessuffer from delays and cost overruns as a result of poorlabor productivity. Alinaitwe (2006) confirms that thepoor productivity of craftsmen is a prime cause of costand time overruns on building projects. Constructionlabor productivity has become such a word and one ofthe most frequently researched topics because in mostcountries, labor cost comprises 30 to 50% of the overallproject’s cost and thus is regarded as a true reflectionof the economic success of the operation (Prabhu andAmbika 2013).Since labor cost represents a considerable propor-

tion of the final cost of the building and is usuallybetween 40 to 60 percent of the building cost, laboris acknowledged as the most important factor of pro-duction (Adamu et al. 2011). Moreover, because itcreates value and sets the general level of productivity,labor productivity is usually adopted as an index formeasuring overall productivity.

1.1 Concreting and Construction IndustryProductivity

Concrete work is an important and fundamental part ofmodern construction practice common to internationalconstruction and can provide a meaningful indicationof the comparative performance of contractors sinceit is essentially a cyclical task similar on all construc-tion sites, regardless of international location (Proverbset al. 1999). According to Wang and Anson (2000), thelevel of development of concreting in terms of size andperformance may be taken as an index representativeof the development level of a particular constructionindustry taken as a whole. Ameh and Odusami (2003)also establish that the cost of concreting operation issignificant to the overall construction cost in an econ-omy.The annual global production of concrete hovered

around 11 billion metric tons (Mehta and Monteiro2006) with the construction industry being the majorconsumer. According to Graham et al. (2005), the pro-duction rates of concrete are on the increase, signifyinga continuous reliance on it by the construction indus-try. Concrete placement is thus a massive operation in

the construction industry and the operational produc-tivity of equipment and labor in concrete placementis an essential, intrinsic parameter influencing wholeconstruction industry (Chan and Kumaraswamy 1995;Wang et al. 2001). Concreting is also one of the mostcommon operations in today’s construction industryand concrete operations including batching, transport-ing, and placement, are familiar in many constructionsites throughout the world. In terms of the most com-mon fields of research investigations, concreting oper-ations come second only to excavation works (Panasand Pantouvakis 2010).Although in Nigeria there is still shortage of accurate

data on overall demand and production of concrete, O-laoluwa (2013) maintains that concreting and concreteplacement in Nigeria, like in most of other countries,have always played an important supportive role in thegrowth of the Nigerian construction industry because,a typical Nigerian building has concreting in virtuallyall of its elements. Furthermore, concreting takes upabout 15% of the total frequency of construction oper-ations and infrastructural construction investment inNigeria which is currently estimated at over USD 5billion annually (Olaoluwa et al. 2012).

1.2 Objectives of the Study

Several empirical studies have revealed that outputin Nigerian construction is rather low when comparedwith many developed countries, and workers’ produc-tivity on sites is also poor (Fagbenle et al. 2004). Inview of this, the study therefore examined labor pro-ductivity in concreting to provide insights into areas ofenhancement for concrete production and placementin Nigeria. The objectives were to measure productiv-ities achieved by site labor using the different concreteplacement methods and to identify the factors influenc-ing labor productivity generally in concreting process.The study also developed a model for evaluating thesignificance of the identified factors and determined theproductivity improvement by producing benchmark re-sults for comparison with productivity of local contrac-tors and contractors in other countries. Such modelingand benchmarking provides management with invalu-able feedback for daily decision-making, and ultimatelymake management more competent at improving pro-ductivity and performance over time.

2 REVIEW OF RELEVANTLITERATURE

2.1 Concrete Placement Methods

The methods employed for loading and unloading con-crete vary widely from site to site, depending on siteconditions, pour characteristics, and availability ofplacement equipment. (Lu et al. 2003) and Lu and An-son (2004) submit that in Hong Kong and most parts

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of the developed world, six concrete placement meth-ods as classified by the leading placement equipmentsuch as backhoe, direct tip, hoist and barrow, pump,crane and 1-skip and crane and 2-skip are used whileOlaoluwa (2008) reports that in Nigeria, four majorconcrete placement methods - head pan, wheelbarrow,dumper, and crane and 1bucket/skip - are used besidesthe pump which is used occassionally.Many Nigerian buildings are still constructed using

the traditional method of in-situ concrete placementin which concrete is site-batched and mixed and theuse of ready-mixed concrete via pumps, truck mix-ers and conveyor belts is almost non-existent. Headpans and wheelbarrows serve as the standard place-ment unit with women and unskilled hands using themto haul manually mixed concrete on building sites be-cause Nigeria has been slow to welcome technologicalchanges.

2.2 Concreting Productivity Rates

Having been established that productivity rates arecrucial figures needed to study construction productiv-ity, an accurate estimate of the productivity for in-situconcreting operations is desirable for planning purposes(Forbes and Ahmed 2011). Planning engineers requireproductivity rates to estimate and schedule pours, re-source levels, and accounting control (Proverbs et al.1999). They maintain a large databank of basic pro-ductivity rates adjustable for individual projects con-sidering specific site factors and conditions that couldinfluence the productivity of construction operations(Dunlop and Smith 2003). Hence, planners could oftenmodify their productivity rates for a specific estimateto reflect anticipated delay times.While it is difficult to define a standard productivity

measure because different companies use internal sys-tems that are unstandardized, productivity can be sim-ply illustrated by an association between output andinput. Park (2006) explains that two forms of produc-tivity are used in the industry:

• productivity = output/input and• productivity = input/output.

The first form has been widely used in construction andexisting literature while the second is often employedfor estimating. Park (2006) identifies two variables incalculating productivity: “Total Factor Productivity”and “Single Factor Productivity”.Total factor productivity (TFP) or multi-factor pro-

ductivity usually takes multiple factors such as labor,equipment, materials, and capital as input, and is oftenapplied more in economic studies than in construction.Frequently defined for conceptual and analytical sim-plification in construction, trade productivity is con-strued as the ratio of output to tradesman’s input ina particular trade, and can be expressed in quantita-tive terms as physical productivity. However, Wang

(1999) postulates the importance of specifying the in-put and output to be measured when deriving produc-tivity because input parameters such as labor, tools,capital, equipment, materials, and design to the con-struction system, vary significantly. For an operationlike concreting, with well-known equipment and workmethods, construction productivity estimation can bechallenging because of the unique work requirements,changeable environments for each project, and com-plexity of influences of job and management factors onoperational productivity (Ok and Sinha 2006).Different yardsticks are usually applied to mea-

sure concrete placement productivity, generally giving“Placing Labor” or “Equipment Productivity” as theratio between quantity of concrete placed and man-hours (mh) or equipment hours (eh) committed by theplacement gang or equipment respectively; the “Mix-er Productivity” as the ratio between the quantity ofconcrete placed to the mixer-hours spent on site (Wang1995; Anson et al. 1996). Concreting productivity con-sequently entails relating a single input (worker-houror equipment-hour) to a single output (concrete vol-ume in m3) and the simple productivity ratio of thisinput and output is calculated assuming a closed sys-tem with all other factors held constant except for thedesired input and output (Wang 1999; Jugdev et al.2001). Such productivity measures relating output sep-arately to each major class of input proportions reflectchanges in these input proportions as well as changes inproductive efficiency, allowing organizations to analyzechanging costs of inputs when combined or separated,in terms of prices and quantities.Overall productivity for an entire concreting opera-

tion (the “Placement Rate”) is thus appropriately mea-sured as the ratio of concrete volume placed to thetotal time of operation in m3/hr (Wang et al. 2001;Olaoluwa and Adeyemi 2009; Olaoluwa et al. 2012).This study however adopts the concept of measur-ing labor productivity as input/output or operativehours per work unit (wh/m3of concrete), commonlycalled the unit rate, because it proved more appropriatefor planning and estimating purposes (Proverbs et al.1999; Thomas et al. 1999; Dunlop and Smith 2003).

2.3 Factors Influencing Concreting Pro-ductivity

According to the Intergraph White Paper (2012), goodconstruction planning should consider and track laborfactors in the original work scope to accurately reflectall conditions used to estimate and fund a project whilealso eliminating/minimizing the impact on productiv-ity that directly affects construction costs. It shouldalso include changes in work scope that look at laborimpacts as part of the sequence and planning of anywork.Technological advances make labor productivity

management more predictable as they help planners

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manage and control labor impacts through improvedflexibility of project labor factors. Engineers shouldtherefore be well-versed in construction technology,knowledgeable in plant performance/availability withrespect to labor, and possess extensive experiencewith factors affecting performance and requirements(Proverbs et al. 1999).Construction planners must determine effective con-

struction methods and completion times. For in-situconcrete structures, such determinations include trans-portation systems, temporary works provisions (scaf-folding, formwork, etc.), methods of reinforcement fab-rication, and labor utilization factors (Proverbs et al.1997; Proverbs et al. 1998; Vikan 2008). Past studieshave researched factors affecting general labor produc-tivity in Nigerian construction but few have consideredor published details of resource and on-site manage-ment factors affecting labor productivity in concretingoperations. These factors tend to have more poten-tial value than motivational influences because withoutthem being addressed, it is futile pursuing any otherproductivity drive (Olomolaiye and Ogunlana 1989).Findings by Proverbs et al. (1999) indicate that cer-

tain construction methods/resource utilization factorsimpact the productivity of principal high-rise concret-ing operations and some models have been developedusing regression analysis to provide qualitative evalua-tions of the impact of different factors on constructionlabor productivity (Sonmez and Rowlings 1998; Janget al. 2011). Amaratunga et al. (2002) however sub-mits that a quantitative research allows flexibility ofdata treatment in terms of comparative and statisticalanalyses and repeatability of data collection in order toverify reliability. This has the advantage of measuringthe subject under analysis through objective methodsrather than subjectively via sensation, reflection or in-tuition.According to Jang et al. (2011), labor productiv-

ity in construction industry plays an important rolein cost estimation, scheduling, and planning. It is in-fluenced by various factors whose impact are quantifi-able in productivity models. Thus, some models weredeveloped using regression analysis for the evaluationof the impact of varied factors affecting productivityof concrete placement, including placement technique,pour size/shape/type, weather, pour location or dis-tance, and the ability to supply concrete to the sitewhen needed as measured by interruptions or delaysin concrete placement activity (Wang and Anson 1994;Anson and Wang 1994; Wang 1995; Anson et al. 1996;Wang et al. 2001; Dunlop and Smith 2003; Lu and An-son 2004). These models, detailed with performanceyardsticks, have been established for Hong Kong andBeijing and are useful not only for comparative pur-poses, but also for planning concrete supply and place-ment and for other purposes (Wang 1995; Dunlop andSmith 2003). Previous findings by (Anson and Wang1994; Anson and Wang 1998; Chan and Kumaraswamy

1995; Proverbs et al. 1999; Wang et al. 2001; Luand Anson 2004; Dunlop and Smith 2003; Jang et al.2011) also indicate the effects of the placement methodand labor utilization factors on concreting productivityacross Europe, Hong Kong, and Singapore.This paper builds and expands on these previ-

ous works by identifying concrete placement methodsprevalent in and peculiar to Nigeria and focusing on ex-amining labor productivity rates and factors in thesemethods. A quantitative methodological approach hasbeen adopted to examine labor productivity of the var-ious concrete placement methods, the factors influenc-ing the productivity rates, and the significance of thefactors influencing labor productivity in the concretingprocesses.

3 RESEARCH METHODOLOGY

The study targeted all the bungalow and single-storybuilding sites in Lagos metropolis where considerablein-situ concreting was being carried out and within theconstraints of time and money available, the hand-delivered interviewer-denominated questionnaire wasadopted for the survey. The questionnaires were pilottested on all ongoing projects to identify 64 buildingsites manned by contractors duly registered with theNigerian Federal Ministry of Works and formally ad-judged capable of concreting to acceptable standards.

3.1 Sampling Design and Sample Frame

Stratified random sampling procedure, which is an ap-plied random sampling method, was used. The con-struction sites were stratified into:

• Sites manned by large firms, for example, thoseregistered in Category A with the Federal Min-istry of Works;

• Sites manned by medium firms, for example,those registered in Categories B & C with theFederal Ministry of Works; and

• Sites manned by small firms, for instance, thoseregistered in Category D with the Federal Min-istry of Works;

This was to ensure that the samples selected fromthe strata are independent, mutually exclusive groupswhich are relevant, appropriate and meaningful.Of the 64 sites identified, 25 were selected for de-

tailed productivity study of their concreting operationsthrough stratified random sampling method as follows:

• 5 sites manned by large sized construction firmsregistered in category A with the Federal Min-istry of Works.

• 10 sites manned by medium sized constructionfirms registered in categories B and C with theFederal Ministry of Works and

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• 10 sites manned by small sized construction firmsregistered in category D with the Federal Min-istry of Works.

3.2 Data Collection and Field Work

The structured survey sheet was developed to gath-er primary data on the concreting operations to en-sure consistency of approach while making allowancesfor general discussions and peripheral comments thatwere noted and added to support contextual evidence.The survey sheets were duly completed during person-al site visits, backed up with face-to-face discussionswith site personnel and operatives; hence, non-responsewas not a serious problem in this study. The data ap-propriate for the productivity study of the concretingoperations (the activities of mixing, transporting and

placing) were obtained through site survey and directobservation of the concrete pours on the 25 buildingconstruction sites. Direct measurements were madeover the cycles of concreting operations to obtain op-erational data on each of the concrete pours.The obser-vation technique involved collecting labor inputs thatwere cross-checked with superintendents and foremenfor verification and accuracy upon the completion ofthe activity.

3.3 Observed Concrete Operations

A total of 167 separate concrete operations were ob-served from beginning to end on the 25 project sites.The operations comprised 11 pump pours, 35 crane andskip pours, 26 dumper pours, 58 wheelbarrow pours,and 37 head pan pours.

Table 1. Observed data and calculated productivity characteristics for each placement methodand each type of pour

Placement Type Pour Delay Total Fract Number of Distance Overall Worker-Method of Pour Size (mins) Duration Delay Operatives to Pour Producti- hour per

(m3) (hr) Location(m)

vity per(m3/hr)

m3

Pumping Beam Sum 467.6000 470.00 48.66 1.03 194 175.00 112.69 33.62& slab Mean 51.955556 52.2222 5.4070 .1146 21.56 19.4444 12.5212 3.7350

Nb/s 9 9 9 9 9 9 9 9Column Sum 48.0000 .00 4.67 .00 41 38.00 21.94 3.65& wall Mean 24.000000 .0000 2.3350 .0000 20.50 19.0000 10.9701 1.8225

Nc/w 2 2 2 2 2 2 2 2Total Sum 515.6000 470.00 53.33 1.03 235 213.00 134.63 37.26

Mean 46.872727 42.7273 4.8485 .0938 21.36 19.3636 12.2392 3.3873Np 11 11 11 11 11 11 11 11

Crane Beam Sum 1319.2000 1222.88 92.72 4.31 443 278.00 333.24 55.21& Skip & slab Mean 59.963636 55.5855 4.2146 .1958 20.14 12.6364 15.1473 2.5096

Nb/s 22 22 22 22 22 22 22 22Column Sum 136.4480 523.29 34.08 2.35 168 10.00 60.19 88.02& wall Mean 10.496000 40.2531 2.6217 .1804 12.92 .7692 4.6300 6.7710

Nc/w 13 13 13 13 13 13 13 13Total Sum 1455.6480 1746.17 126.80 6.65 611 288.00 393.43 143.23

Mean 41.589943 49.8906 3.6229 .1901 17.46 8.2286 11.2409 4.0924Nc/s 35 35 35 35 35 35 35 35

Dumper Beam Sum 411.8000 1048.53 80.73 5.05 334 715.00 212.79 115.02& slab Mean 17.904348 45.5883 3.5101 .2194 14.52 31.0870 9.2519 5.0010

Nb/s 23 23 23 23 23 23 23 23Column Sum 34.6300 179.85 14.26 .58 47 45.00 9.07 18.33& wall Mean 11.543333 59.9500 4.7525 .1937 15.67 15.0000 3.0220 6.1105

Nc/w 3 3 3 3 3 3 3 3Total Sum 446.4300 1228.38 94.99 5.63 381 760.00 221.86 133.35

Mean 17.170385 47.2454 3.6534 .2164 14.65 29.2308 8.5331 5.1290Nd 26 26 26 26 26 26 26 26

Wheel Beam Sum 2723.2000 2941.89 354.84 7.24 900 613.80 374.12 364.70Barrow & slab Mean 55.575510 60.0386 7.2417 .1477 18.37 12.5265 7.6351 7.4429

Nb/s 49 49 49 49 49 49 49 49Column Sum 67.1680 89.14 39.58 .64 127 68.00 14.04 200.16& wall Mean 7.463111 9.9044 4.3973 .0715 14.11 7.5556 1.5597 22.2400

Nc/w 9 9 9 9 9 9 9 9Total Sum 2790.3680 3031.03 394.42 7.88 1027 681.80 388.16 564.86

Mean 48.109793 52.2591 6.8004 .1359 17.71 11.7552 6.6924 9.7390Nwb 58 58 58 58 58 58 58 58

Headpan Beam Sum 914.9200 1133.18 214.36 3.16 492 379.40 105.58 253.33& slab Mean 35.189231 43.5838 8.2445 .1214 18.92 14.5923 4.0609 9.7436

Nb/s 26 26 26 26 26 26 26 26Column Sum 65.9300 605.89 49.47 2.86 152 77.70 13.20 194.72& wall Mean 5.993636 55.0809 4.4971 .2604 13.82 7.0636 1.1997 17.7018

Nc/w 11 11 11 11 11 11 11 11Total Sum 980.8500 1739.07 263.82 6.02 644 457.10 118.78 448.05

Mean 26.509459 47.0019 7.1304 .1627 17.41 12.3541 3.2103 12.1095Nhp 37 37 37 37 37 37 37 37

Total Beam Sum 5836.7200 6816.48 791.32 20.78 2363 2161.20 1138.43 821.88& slab Mean 45.245891 52.8409 6.1342 .1611 18.32 16.7535 8.8251 6.3712

Nb/s 129 129 129 129 129 129 129 129Column Sum 352.1760 1398.17 142.05 6.43 535 238.70 118.43 504.88& wall Mean 9.267789 36.7939 3.7382 .1693 14.08 6.2816 3.1166 13.2863

Nc/w 38 38 38 38 38 38 38 38Total Sum 6188.8960 8214.65 933.37 27.21 2898 2399.90 1256.86 1326.76

Mean 37.059257 49.1895 5.5890 .1630 17.35 14.3707 7.5261 7.9447NT 167 167 167 167 167 167 167 167

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Table 1 shows data and productivity characteristicsobserved and calculated for all 167 concreting opera-tions. It indicates for each type of pour and placementmethod employed, the pour size or quantity of concreteplaced, the total duration of pour (overall pour timefrom the beginning of each operation to the end), thetotal time of delay and the distance to pour location,that is, distance between the pour location and themixer/loading position. The total time of delay com-prised the idle times encountered during the concretingoperation due to poor weather, plant breakdowns, fuelor material shortages and other problems relating todifficulties in concrete mixing and placement. The cal-culated quantities are the fractional delay (delay timeexpressed as a decimal fraction of the pour duration)as well as the overall productivity (m3/hr) and laborproductivity (workerhr/m3) values indicated in the lasttwo columns of the table. Labor productivity is calcu-lated as the ratio of the operative hours committed bythe concreting gang to the quantity of concrete (m3)placed in each pour. For each placement method, asshown in Table 1, it shows the sum (total) and meanvalues of the observed and measured quantities, thenumbers of beam and slab (Nb/s), column and wall(Nc/w), pump (Np), crane and skip (Nc/s), dumper(Nd), wheelbarrow (Nwb), headpan (Nhp) pours, totalnumber (NT ) and the gross total for all pours.Table 2 is a summary of the values of the observed

and calculated characteristics for all pours by each

placement method and for all pours aggregated to-gether. The mean pour size for all 167 pours in thesample was 37m3. The biggest mean pour size was48m3 placed by wheelbarrow followed by about 47m3

and 42m3 respectively for pump and crane. The meanpour size for concrete placed with head pan (26.5m3)was about half the size placed by wheelbarrow while themean pour size for dumper was the smallest at 17.2m3

showing that head pan and dumper were generally usedwhen the quantities of concrete placed were least. Thiscould be due to the small size of the head pan and thefact that it is the most primitive and labor intensiveof the placement methods while the dumper is general-ly restricted to ground floor and pavement pours only.The biggest mean pour size of 48m3 placed by wheel-barrow may be due to its popularity and predominantusage in 58 out of 167 (35%) of the pours.The mean duration of all pours was found to be

around 51/2 hours. The longest mean duration ofabout 7 hours was for pours placed by head pan andwheelbarrow while the mean duration for pours placedby crane and dumper were almost equal at about 31/2hours or about half the duration for pours placed byhead pan and wheelbarrow. This is reasonable becauseconcreting with crane and dumper is more mechanizedand faster and therefore expected to take shorter time.On the other hand, the long duration of about 5 hoursfor the pump must be due to the fact that it is re-stricted to the few (11 out of 167) large pours where

Table 2. Summary of observed data and calculated productivity characteristics for each placement method

Placement Pour Delay Total Fract Number of Distance Overall Worker-Method Size (mins) Duration Delay Operatives to Pour Producti- hour per

(m3) (hr) Location (m) vity per(m3/hr)

m3

Pumping Sum 515.6000 470.00 53.33 1.03 235 213.00 134.63 37.26Mean 46.872727 42.7273 4.8485 .0938 21.36 19.3636 12.2392 3.3873Np 11 11 11 11 11 11 11 11

Crane Sum 1455.6480 1746.17 126.80 6.65 611 288.00 393.43 143.23& Skip Mean 41.589943 49.8906 3.6229 .1901 17.46 8.2286 11.2409 4.0924

Nc/s 35 35 35 35 35 35 35 35Dumper Sum 446.4300 1228.38 94.99 5.63 381 760.00 221.86 133.35

Mean 17.170385 47.2454 3.6534 .2164 14.65 29.2308 8.5331 5.1290Nd 26 26 26 26 26 26 26 26

Wheel Sum 2790.3680 3031.03 394.42 7.88 1027 681.80 388.16 564.86Barrow Mean 48.109793 52.2591 6.8004 .1359 17.71 11.7552 6.6924 9.7390

Nwb 58 58 58 58 58 58 58 58Headpan Sum 980.8500 1739.07 263.82 6.02 644 457.10 118.78 448.05

Mean 26.509459 47.0019 7.1304 .1627 17.41 12.3541 3.2103 12.1095Nhp 37 37 37 37 37 37 37 37

Total Sum 6188.8960 8214.65 933.37 27.21 2898 2399.90 1256.86 1326.76Mean 37.059257 49.1895 5.5890 .1630 17.35 14.3707 7.5261 7.9447NT 167 167 167 167 167 167 167 167

Table 3. Summary of ANOVA results to test the effect of categorical concrete placement/resource factorson concreting productivity

Concrete placement Mean Labor productivity rates (wk-hr per m3)resource utilization factors F statistic P valuePlacement/transportation methods 3.66 0.0070*Types of pour 11.52 0.0009*Weather 0.41 0.7483

Note: ∗ - significant at 99% level.

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its usage is economically justified.The mean number of operatives for all the concrete

operations was 17 and was about the same as the meannumber of operatives for pours placed by crane, wheel-barrow and head pan. Only the mean number of opera-tives employed for pours placed by dumper was a littlebit lower at 15 while the mean number of operativesemployed for pours placed by pump is very high at 21suggesting that there might not have been proper plan-ning or work scheduling effort to correlate the numberof operatives required with the placement method andensure optimal utilization of labor.Delays observed in all the operations were within the

range of 50 ± 3 minutes for all the placement meth-ods showing that the overall delays are virtually con-stant and do not correlate with the placement method.They are apparently, mostly materials/equipment- andlabor-related delays due to poor co-ordination & plan-ning and lack of adequate control of site activities.For all pours, the mean distance between the mix-

ing/batching point and the pour location was about14.5 meters. This distance was longest (about 30 me-ters) for pours placed by dumper, shortest (8 meters)for pours placed by crane and approximately 12 metersfor pours placed by both the wheelbarrow and the headpan. This is reasonable because the dumper is usuallyrequired for transporting concrete over long horizon-tal distances where the head pan and the wheelbarroware at disadvantage while the pump and crane are usedmainly for lifting and placing concrete to high levels.

4 ANALYSIS OF DATA

Statistical analyses were conducted by the Stata statis-tical software package for Windows (version 12, 2011)to determine the impact of identified concrete place-ment/resource utilization factors on labor productivi-ty rates. The concrete placement/resource utilizationfactors were:

• Concrete placement/transportation methods• Types of pour• Size of pour• Number of operatives• Working time or total duration• Waiting or delay time• Fractional delay• Distance to pour location and• Weather

4.1 One-Way Analysis of Variance

Analysis of Variance (ANOVA), which is a linearregression with categorical predictor variables, wasfirst employed to test the hypothesis that the meanconcrete placement labor productivities attributed tothe categorical factors as variables were significant-ly different. For example, using the concrete place-ment/transportation methods (pump, crane, dumper,

wheelbarrow and headpan) as the dependent variable,variance in the mean labor productivity rates were in-vestigated. The results of this test, shown in Table 3 (Fstatistic = 3.66, Probability, P value = 0.007) indicatethat there is a significant difference (>99% significancelevel) among the means of labor productivity rates foreach placement method sampled. This procedure wasrepeated for the two types of pour (beam/slab andcolumn/wall) and the four weather conditions (fine,cloudy, sunny, rainy) as the dependent variables forANOVA of mean concreting labor productivity and allthe results are provided in Table 3. The results thatare significant at 99% level are indicated with an aster-isk, showing that there is significant difference in laborproductivity rates for only placement method and typeof pour.Tables 4 and 5 provide descriptive statistics for the

significant categorical variables of placement methodand type of pour, including mean and minimum (mostefficient) productivity rates. Both tables indicate thatvariations in the labor productivity rates (represent-ed by coefficient of variation) are very high for eachplacement/transportation method and type of pour.There is, however, some inconsistency between themean and minimum productivity rates where the low-est minimum (most efficient individual) productivityrate (0.170wh/m3) was attributed to crane as againstthe lowest mean overall producivity (most productive)rate (3.387wh/m3) for pump. Productivity values fortypes of pour in Table 5 however indicate that beamand slab pours consistently yielded lower mean andminimum productivity rates than column and wallpours.

4.2 Regression Analysis

Regression analysis was carried out as an extension ofthe one-way ANOVA, t tests, to produce the regres-sion Table 6 and explain the proportion of variabilityin labor productivity rates due to all the variables ofconcrete placement/resource utilization factors takenas a set.The table shows the estimated partial regression co-

efficients and the corresponding t-statistics from theregression on labor productivity for all the explanato-ry variables.As can be seen from the table, the signif-icant variables are those for which the p-values of thet-statistic are less than 0.05 and are indicated with anasterisk. Table 7, the ANOVA table of regression onlabor productivity shows the results of the F -test forthe overall significance of all coefficients. The P -value,which is the probability that the F -statistic is greaterthan the critical value, is less than 0.05 (=0.0000) andshows that the coefficients of the regression model arejointly significant. This is also reflected by the AdjR-squared value of 0.2656 which is considered goodenough because it is greater than 0.25.A further indication of how the regression model fits

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the data is demonstrated in Figure 1 where the ac-tual labor productivity values are plotted against thevalues derived or fitted from the regression equation.Although 16 (i.e. 9.5%) of the plots should be discard-ed since they return negative fitted labor productivityvalues, the figure shows a linear trend on 1:1 slope witha close fit of most of the plots indicating that the val-ues derived from the model are practically equal to theobserved values of actual labor productivity.Furthermore, since one of the most important as-

sumptions of the regression model is that the variabilityof the data does not change for different levels of theresponse or explanatory variables,another check to val-

idate the model was carried out to verify that this as-sumption of constant variance is satisfied. If the modelfit to the data were correct, the residuals would be therandom errors that make the relationship between theexplanatory variables and the response variable a sta-tistical relationship. If the residuals appear to behaverandomly, it suggests that the model fits the data well.On the other hand, if non-random structure is evidentin the residuals, it is a clear sign that the model fitsthe data poorly.The difference between the observed value of the de-

pendent variable (y) and the predicted value (y) iscalled the residual (e), each data point having one

Table 4. Concrete labor productivity rates for different placement/ transportation methods

PlacementFrequency

Summary of productivity (wk-hr per m3)method Mean Minimum Coefficient of Variation (%)Pump 11 3.387 0.747 99. 8Crane & skip 35 4.092 0.170 144Dumper 26 5.129 0.598 101Wheelbarrow 58 9.739 0.444 152.5Headpan 37 12.109 1.229 99Total 167 7.945 143.3

Table 5. Concrete labor productivity rates for different pour types

Type Frequency Summary of productivity (wk-hr per m3)of pour Mean Minimum Coefficient of Variation (%)Beam & slab 129 6.371 0.170 132.3Column & wall 38 13.286 0.652 129.83Total 167 7.945 143.3

Table 6. Estimated partial regression coefficents and the corresponding t-statistics from the regressionon concreting labor productivity (wk-hr/m3) for all variables

Variable Coefficients t-statistic P>|t|Placement method 2 .0302738 0.01 0.993Placement method 3 .3754875 0.10 0.918Placement method 4 6.643384 2.01 0.047*Placement method 5 6.033435 1.71 0.089Type of pour 2 7.042321 3.50 0.001*Weather 2 -1.370226 -0.46 0.648Weather 3 -4.761756 -0.75 0.457Weather 4 2.375492 1.09 0.278Size of pour -.0809076 -4.95 0.000*Waiting time -.0006198 -0.03 0.980Total duration .4687338 1.48 0.142Fractional delay 12.98304 1.92 0.056Number of operatives .291793 3.60 0.000*Distance to pour location .0320234 0.64 0.526Intercept -4.81727 -1.27 0.207

Note: ∗ - significant at 99% level.

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Figure 1. Plot of actual labor productivity against fitted labor productivity for linear regression model

residual, such that residual is equal to the substrac-tion of observed value and predicted value as

e = y − y (1)

A residual plot is a graph that shows the residual-s on the vertical axis and the independent variable orpredicted value on the horizontal axis. Residual plotscan be used to assess the quality of the regression. Ifthe points in a residual plot are randomly dispersedaround the horizontal axis, a linear regression model isappropriate for the data; otherwise, a non-linear modelis more appropriate. For the underlying statistical as-sumptions to hold true for a particular regression mod-el, the residuals would have to be randomly distributedaround zero.

If the regression assumptions hold – that is, the da-ta are distributed normally – about 95% data pointsshould fall within 2σ around the fitted curve. Conse-quently, 95% of the standardized residuals will fall be-tween -2σ and +2σ in the residual plot.Thus,generally,if the residual, ei , is the difference between the ob-served response, yi , and the predicted or fitted value,ý i ,and if the constant variance assumption holds, theresiduals will follow an N(0, σ2) distribution and aplot of the residuals for each i against the fitted val-ues, ýI .,should follow a random pattern with 95% ofthe points lying within a 2σ horizontal band aroundzero.Figure 2 is a plot of the residuals from the regression

analysis against the fitted values of the regression equa-

Figure 2. Residual plot for linear model on labor productivity

Table 7. ANOVA statistics for regression on labor productivity (wk-hr/m3)

Degrees of freedom Sum of Squares Mean Squares P>FRegression model 14 7046.65327 503.332377 0.0000Residual 152 14466.6588 95.1753869Total 166 21513.3121 129.598266Adj R-squared 0.2656

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tion which shows that no non-random pattern exists.Since the mean square of the residual in the ANOVA(95.1753869), is an estimator for σ2 , it can be deter-mined from Table 7 that most of the residuals shouldfall between ±2

√95.18 or ±19.51. Figure 2 shows that

this is true for this study as only 5 points or about3% of the 151 valid observations (i.e. total observa-tions less 16 that return negative fitted labor produc-tivity values) lie outside this range (between the redhorizontal lines) confirming that the constant varianceassumption is satisfied.Finally, the multiple linear regression model that

can be given for actual labor productivity (worker-hour/m3) in all the concreting operations from Table7 is labor productivity as

PL = 6.64Pm4 + 7.04Tp2 − 0.08Sp + 0.29On (2)

where Pm4 = placement method 4 (wheel barrow);Tp2 = type of pour 2 (column/wall); Sp = size ofpour;On = number of operatives.This regression model was further validated using

34 concrete pours with similar placement methodsand pour types (i.e.beam/slab and column/wall) fromprojects within the same study area of Lagos State,Nigeria, for it to be of practical use on constructionprojects. The predicted labor productivities using theabove derived regression model are compared to theactual labor productivities achieved on the 34 concreteoperations in Figure 3.The figure generally shows encouraging predictions

as about 20 pours or 60% of the pours return less than15% difference between the predicted and actual laborproductivities while 25 pours or 75% of pours returnless than 20% difference between the predicted and ac-tual labor productivities. Five (5) or 15% of the pours- 8, 11, 25, 27, and 29 - provide the least accurate pre-dictions using the model with the percentage differencebetween the predicted and actual labor productivitiesgreater than 25% and the model oveerestimating allthese pours numerically. The actual productivities of 4

out of these 5 pours (i.e., excluding pour 25), are about5 whr/m3 or less. Pours 8 and 11 are the lowest numer-ically and the model oversetimates their productivitiesof about 0.72 whr/m3 by 35%. Considering that thedata-set used to derive the regression model containedonly 10% of pours with numerical productivities lessthan 0.72 whr/m3while 25% of the validation pourshave numerical productivities below 0.72 whr/m3, thederived model could have been poor at predicting suchnumerically low labor productivities.The actual labor productivity of pour 25 which the

model overestimates by about 37% is about 8 whr/m3.This is however a column/wall pour which normallyreturns low productivity due to the slow, controlledmanner in which concrete has to be poured into thestructure and it is likely the regression model couldnot model this specific characteristic of the pour shapeadequately.

5 RESULTS AND DISCUSSIONS

For all pours, labor productivity calculated in workerhour/m3 for each type of pour and for each placingmethod are shown in Table 1, while Table 2 summa-rizes these labor productivities for the 167 pours andfor each placement/transportation method.

5.1 Concrete Placement Methods and La-bor Productivity Values

In this study, it has been found that the most prevalentconcrete placement methods used, in order of usage arewheelbarrows, head pans, cranes, dumpers and pumps.This contrasts with pumps (Anson et al. 1989); pumpsand cranes (Chan and Kumaraswamy 1995); pumps,cranes, hoist & barrow, and tremies (Anson and Wang1998; Lu and Anson 2004); and pumps, cranes andtremies (Wang et al. 2001), used in Asian and Euro-pean countries where headpans and wheelbarrows arehardly used alone.

Figure 3. Plot of predicted and actual labor productivity for pours used for validation

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Table 2 shows that for all 167 concrete pours in thisstudy, the mean labor productivity observed is 7.94wh/m3 which contrasts sharply with the labor produc-tivities of:

• 0.49wh/m3 observed in 154 pours in Hong Kong(Anson and Wang, 1998)

• 0.53wh/m3 observed in 32 pours in Singapore(Wang Ofori and Teo 2001)

This indicates that overall, concreting labor produc-tivity in Nigeria is an abysmally low value of about 15- 16 times less than those obtained in Hong Kong andSingapore. This is chiefly attributable to poor place-ment methods (headpans & wheelbarrows) used in over50% (95 out of the 167) of observed concreting opera-tions.The mean concreting labor productivity of 7.94

wh/m3 obtained in this study is 20% higher than the9.58 wh/m3 observed in 18 sites in Lagos by Ameh andOdusami (2003) in their output study of in-situ con-creting operations in Lagos State. Interestingly, their9.58wh/m3 value compares favourably with 9.74wh/m3

obtained in this study for wheelbarrow and gives anindication that Ameh and Odusami (2003) study mayhave been conducted on in-situ concreting operationsplaced by wheelbarrow.

5.2 Effect of Placement Method

From this study, it is observed that placement methodis a major determinant of labor productivity for con-crete pours, as shown in Tables 2 and 3. This confirmsthe findings of Anson and Wang (1998) and Dunlopand Smith (2003) that placement method is the primemover governing concreting productivity. For exam-ple, labor productivity in this study is found to increase(i.e., less worker hours are required to place 1m3 of con-crete) with improvement in the placement method asindicated by the following mean labor productivities:

• 12.11 wh/m3 for head pan,• 9.74 wh/m3 for wheelbarrow,• 5.13 wh/m3 for dumper,• 4.09 wh/m3 for crane, and• 3.38 wh/m3 for pump.

These values show that the labor productivities forconcreting in Nigeria are approximately in the ra-tio 1:1.2:2.4:3:3.6 for headpan, wheelbarrow, dumper,crane and pump respectively. Notably, there are sig-nificant differences between the productivity rates ofheadpan and pump, headpan and crane, and headpanand dumper but relatively no substantial differencesbetween the productivity rates of headpan and wheel-barrow and between the productivity rates of pump,crane and dumper.It is noteworthy that the labor productivities of 3.38

wh/m3 and 4.09 wh/m3 for pump and crane observedin this study are about seven and five times lower than

0.47 wh/m3 and 0.81 wh/m3 obtained respectively inAnson and Wang (1998) study of pumped and cranedpours in Hong Kong buildings. This confirms that evenfor the same concrete placement method, concreting la-bor productivity in Nigeria is still much lower than inEurope or Asia.

5.3 Effect of Type of Pour

Tables 1, 3 and 5 indicate that the type of pour, gen-erally reflected in the shape of the structural elementcast, significantly affect concreting labor productivity,with mean productivity in beam/slab (6.37 wh/m3)being slightly over 2 times that of column/wall (13.29wh/m3).These results support the following previous findings:

• Olomolaiye and Ogunlana (1989) on OAU build-ing sites where concreting was done faster in slabsthan in beams and in beams than in columns, andconcreters preferred and could do more in hori-zontal works than in vertical works;

• Anson and Wang (1998) on pumped pours inHong Kong where the concreting productivity ofthick slabs was 29% higher than for columns andwalls;

• Dunlop and Smith (2003) on 202 pumped poursin the UK where concrete productivity for basepours (similar to slab pours) was about 70% high-er than for wall or column pours.

5.4 Effect of Pour size

The regression coefficient of -0.08 in Table 6 for size ofpour indicates that labor productivity increases by 0.8wh/m3 for every extra 10 m3 of pour volume placed.Although slightly lower, this effect of pour size on la-bor productivity is similar and comparable to that ob-tained on total concreting productivity by Olaoluwaet al. (2012) and for the 154 concrete pours in HongKong buildings by Anson and Wang (1998) where to-tal concreting productivity was found to increase by1.1 m3/hr for every extra 10m3 of pour volume.Contractors’ frequent closer attention to site prepa-

ration and work planning to ensure large pours arecompleted during the days they are started (without‘carry-over’) is seen as the major reason that largepours were placed at higher productivities. On theother hand, smaller pours that require less than a dayto execute are usually executed with less diligence andpermitted to take longer hours provided they can stillbe completed within the day of operation.

5.5 Effect of Number of Operatives

Table 6 and the regression equation show that the re-gression coefficient for number of operatives is 0.29 im-plying that labor productivity generally decreases byabout 0.3 wh/m3 for every additional operative puton the concreting operation. The abysmally low labor

36


productivity rates for pump and crane observed in thisstudy as compared to those of the countries of Singa-pore and Hong Kong is therefore attributable to thehigh number of operatives employed for the observedconcreting operations irrespective of the placement andtransportation method.

6 CONCLUSION

This study involving a detailed observation of 167 con-crete pours on Lagos building construction sites hasprovided factual information on productivity figuresdescribing the state of the concreting industry in La-gos, Nigeria.The study investigated the impact of various con-

struction resource/method factors on mean concret-ing labor productivity rates and developed a model toevaluate the significance of these factors in predictingproductivity. The results of actual concrete pours fromdifferent projects were applied to validate the model de-veloped from the regression analysis and the followingfindings were made from the study:

• Labor productivities are approximately in theratio 1:1.2:2.4:3:3.6 for headpan, wheelbarrow,dumper, crane and pump respectively while themean productivity in beam/slab pours is about2 times that of column/wall pours.

• Concrete placement by pump is the least laborintensive of the placement methods and gets thejob completed most quickly but concreting bycrane and skip yields the most efficient produc-tivity rate.

• Labor productivity of in-situ concreting opera-tions is extremely (about 5-7 times) lower inNigeria than in Hong Kong, Singapore or Europe.

• The preference of Nigerian contractors for tradi-tional placement/transportation methods, withpoor professional site management, in particularimproper planning and scheduling of site labor,mirrored these low productivity rates.

• The regression model for actual productivity in-dicates that concreting labor productivity ratesare strongly impacted by placement/ transporta-tion method, pour size, type or shape of pourand the number of operatives. This, however,does not imply that other factors not investigat-ed cannot influence labor productivity.

• The validation exercise demonstrated favourablepredictions of labor productivity while the mod-el derived for estimating labor productivity pro-duces good results for productivities greater than5 wh/m3.

7 RECOMMENDATIONS

The study recommends that:

• Concrete placement by pump and crane be cho-sen as the best options for concreting in Nigeria,especially for large pours, even though their pro-vision may be difficult for many Nigerian Con-tractors, most of whom are small- or medium-sized.

• The use of head pan and wheelbarrow should belimited and restricted to small pour sizes or, atbest, for assistance to pump and crane.

• The placement/transportation method should becommensurate with the quantity of concrete to beplaced and the size of the concreting gang shouldjust be adequate to place the concrete at a speedthat matches the output of the placing equip-ment.

• Long hours such as were observed for wheelbar-row and headpan pours should be avoided be-cause they can result in labor fatigue for workers.

• The various concreting labor productivity ratesobtained should be used as benchmarks for mon-itoring and determining changes in constructionproductivity across the industry. They should al-so be compared with results obtained from otherlocations around the world and used by buildersactively involved in daily in-situ concreting opea-rations to improve their productivity and level ofperformance thus improving the overall produc-tivity of the industry.

• The developed model should be used on buildingprojects managed by different contractors withinthe study area and Nigeria.

• Professional bodies and major construction com-panies and associations should encourage furtherdevelopment of such model and benchmark mea-sures of productivity in the construction industryand publish such measures regularly for use bypractitioners.

• Other variables such as number and cost of loads,average cycle time,average volume, etc,should beincluded in future studies to allow for other oper-ating conditions and obtain comprehensive datafor comparing additional resource utilization fac-tors of different concrete placement methods interms of cost, besides the production of produc-tivity benchmarks.

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