# An Optimization-LCA of a Prestressed Concrete Precast Bridge

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emissions of structures [5,6]. Therefore, reducing the cost implies a reduction of CO

_{2}emissions.

_{2}emissions have been studied by several works. Optimization algorithms are most often used to reduce the cost or CO

_{2}emissions of structures. In some cases, this involves a mono-objective optimization of cost and CO

_{2}emissions [5,6,7], whereas other works carry out multi-objective optimization to achieve both objectives at the same time [8,9]. Despite the relationship between cost and CO

_{2}emissions, the environmental impact cannot be assessed by taking into account CO

_{2}emissions alone [10]. For this reason, the environmental impact assessment must achieve a complete environmental profile. This complete environmental profile can be obtained using the life-cycle assessment (LCA) process. LCA is one of the most important and accepted methods of assessing the environmental impacts [11,12,13,14,15,16], making it an excellent tool for assessing the environmental impact of a bridge.

_{2}emissions indirectly. This process makes it possible to obtain a cost-optimized bridge with a low environmental impact. After finishing the optimization, all the features of the cost-optimized bridge will be known, including its cost but the environmental impact will not yet have been obtained. The LCA makes it possible to obtain a complete environmental profile of this cost-optimized bridge. With this methodology, a bridge whose costs have been optimized directly and whose environmental impact has been improved is obtained and finally the LCA for the whole life-time can be performed. For this purpose, a hybrid memetic algorithm is used to carry out the cost-optimization of the bridge. Then, the Ecoinvent database [17] and the ReCiPe method [18] are used to conduct the LCA process of the bridge.

## 2. Optimization

## 3. Life-Cycle Assessment

## 4. Case Study

#### 4.1. Bridge Description

#### 4.2. Optimization

_{j}are satisfied.

_{1}, x

_{2}, …, x

_{n}are the design variables used for the optimization. The objective function C expresses the cost of the bridge and the restrictions g

_{j}are the serviceability limit states (SLS), the ultimate limit states (ULS), the durability limit states and the geometric and constructability constraints of the problem. There are 40 design variables, including eight variables that define the geometry of the section, two that define the concrete of the slab and the beam, four that define the prestressed steel and 26 that define the reinforcing steel. Furthermore, there are a set of parameters that have no influence on the optimization problem, such as the width, span and web inclination. Structural constraints have been considered according to the Spanish codes [25,26]. The ULSs verify if the ultimate resistance is greater than the ultimate load effect. Besides, the minimum amount of reinforcing steel for the stress requirements and the geometrical conditions are also considered. The SLSs examine different aspects. Cracking limit state requires compliance of the compression and tension cracks, as well as the decompression limit state in the area where the post-tensioned steel is located. Deflections are limited to 1/1000 of the free span length for the quasipermanent combination. In addition, the concrete and steel fatigue has been considered in this study. Table 1 summarizes of the ULSs and SLSs considered.

^{3}/m

^{2}, with a strength of 35 MPa, while the amount of slab concrete used is 0.1797 m

^{3}/m

^{2}, with a strength of 40 MPa. Furthermore, the precast concrete beams require 6163 kg (12.52 kg/m

^{2}) of reinforcing steel and 5184 kg (10.53 kg/m

^{2}) of prestressed steel, while the concrete slab is defined by 11,772 kg (23.92 kg/m

^{2}) of reinforcing steel.

#### 4.3. Life-Cycle Assessment

#### 4.3.1. Goal and Scope

#### Manufacturing

#### Construction

_{2}per m

^{3}of concrete. The distance travelled considered by the construction machinery is 50 km. In addition, the formwork is made by wood and can be reused 3 times.

#### Use and Maintenance

_{2}. On one hand, the bridge needs one maintenance period of 2 days to satisfy with the regulations during its 120 years of service life. This maintenance activity considers that the concrete cover is replaced by a repair mortar. The maintenance action consists firstly of removing the concrete cover and providing a proper surface for the coating adhesion. Then, a bonding coat is applied between the old and new concrete. Finally, a repair mortar is placed to provide a new reinforcement corrosion protection [30]. Note that the study considers that the quality on-site work is adequate to guarantee that the bridge does not have durability problems during the service life. Besides, it is important to highlight that other maintenance activities to repair or replace equipment elements may take place. However, they are not evaluated in this study.

_{2}by the concrete is a widely studied fact [31,32] that has been considered in the bridge studied.

#### End of Life

_{2}is assured. Seventy-one per cent of the steel will be recycled and in this way, the life-cycle of the bridge ends.

#### 4.3.2. Inventory Analysis

#### 4.3.3. Impact Assessment

_{2}emissions are the most popular [35,36]. Despite the importance of the emission of CO

_{2}, a complete impact assessment must consider a set of indicators that represent a complete environmental profile. That implies the use of environmental impact assessment methods. These methods can be separated depending on the approach used: midpoint or endpoint. On one hand, the midpoint approach defines the environmental profile by means of a set of impact categories. One of the most popular methods that take into account the midpoint approach is the CML. On the other hand, the endpoint approach defines the environmental profile considering only a small set of damage categories. One of the most frequently used methods that consider the endpoint approach is the Eco-indicator. Both approaches are necessary to carry out a complete environmental interpretation of the bridge. On one hand, the midpoint approach can provide a more accurate and complete environmental profile. On the other hand, the endpoint approach can be easier to interpret. For these reasons, the environmental impact assessment method used in this work is the ReCiPe method [18], whose main objective is to provide a combination of the Eco-indicator and CML, considering the midpoint and endpoint approaches.

#### 4.3.4. Interpretation

#### Midpoint Approach

_{2}fixed is taken into account. In the GWP impact category, it can be seen that there is a positive impact. On one hand, in the use and maintenance phase, the amount of CO

_{2}fixed is much lower than the CO

_{2}eq produced by the maintenance activities and the traffic detour because the concrete surface in contact with the environment represents a very low proportion of the total of amount of concrete in the bridge. The percentage of the CO

_{2}fixed is −3.84%, while the percentages of maintenance activities and traffic detour are 89.95% and 13.89%, respectively, adding a total of 100% due to that the global GWP impact in this phase is positive. The ratio of the contribution of the maintenance activities and traffic detour can be modified considerably in function of the features of the traffic diversion (distance, average daily traffic and percentage of trucks). On the other hand, in the end-of-life phase, the amount of CO

_{2}fixed is higher (−254.05%) than the CO

_{2}eq produced by the demolition activities (22.40%), the waste treatment (36.21%) and the associated transport (96.18%). The total contribution of the processes in the end-of-life phase is negative, adding a total of −100%. In the other impact categories (PMF and OD), the maintenance activities and transport make the major contribution to each bridge life-cycle.

#### Endpoint Approach

## 5. Conclusions

_{2}emissions are not the only indicator to be considered in the environmental assessment, due to the relationship of this indicator with the cost, it is used to obtain a bridge with the lowest cost and a low environmental impact. Once this bridge has been obtained, a complete environmental assessment is carried out. For this purpose, a heuristic optimization by means of a hybrid memetic algorithm is used to obtain a cost-optimized prestressed concrete precast bridge and thus a low amount of associated CO

_{2}. Then, the midpoint and endpoint approaches of the ReCiPe method are used to obtain a complete environmental profile of the bridge. These different approaches make it possible to obtain complementary data that provide different information. While the midpoint approach provides detailed information, the endpoint approach provides more concentrated information so it is possible to obtain only one score to assess all the environmental impacts.

_{2}emissions are an important indicator in the environmental impact assessment, in some cases it is not sufficient to obtain an accurate environmental evaluation and it is necessary to take into account all the other impact categories.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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Limit States |
---|

Flexure |

Vertical shear |

Longitudinal shear |

Punching shear |

Torsion |

Torsion combined with flexure and shear |

Fatigue |

Crack width <0.2 mm |

Compression and tension stress. Decompression in post-tensioned steel depth |

Deflection for the quasipermanent combination <1/1000 |

Precast Concrete Beam | Concrete Slab | |
---|---|---|

Strength (MPa) | 35 | 40 |

Reinforcing steel (kg/m^{2}) | 12.52 | 23.92 |

Prestressed steel (kg/m^{2}) | 10.53 | – |

Concrete (m^{3}/m^{2}) | 0.1117 | 0.1797 |

Cement (kg/m^{3}) | 300 | 320 |

Gravel (kg/m^{3}) | 848 | 829 |

Sand (kg/m^{3}) | 1088 | 1102 |

Water (kg/m^{3}) | 160 | 162 |

Superplasticizer (kg/m^{3}) | 4 | 5 |

Acronym | Reference Unit | Manufacturing | Construction | Use and Maintenance | EoL | ||||
---|---|---|---|---|---|---|---|---|---|

m | cv (%) | m | cv (%) | m | cv (%) | m | cv (%) | ||

ALO | m^{2} × year | 79.76 | 3.77% | 2.59 | 7.46% | 6.16 | 14.09% | 1.73 | 6.84% |

GWP | kg CO_{2} eq | 1838.55 | 16.86% | 267.85 | 9.61% | 1095.77 | 5.29% | −117.68 | −6.97% |

FD | kg oil eq | 316.90 | 6.90% | 51.48 | 17.52% | 394.59 | 4.94% | 11.00 | 16.57% |

FEPT | kg 1,4-DB eq | 38.15 | 2.93% | 0.93 | 18.86% | 8.53 | 26.70% | 0.19 | 7.94% |

FEP | kg P eq | 0.82 | 4.19% | 0.01 | 10.56% | 0.08 | 14.00% | 0.01 | 7.16% |

HTP | kg 1,4-DB eq | 1470.92 | 3.01% | 22.58 | 16.26% | 110.30 | 16.36% | 5.77 | 7.80% |

IRP | kg U235 eq | 244.70 | 12.29% | 18.96 | 10.35% | 78.57 | 5.22% | 10.22 | 7.14% |

MEPT | kg 1,4-DB eq | 37.90 | 2.92% | 0.96 | 17.91% | 7.65 | 26.08% | 0.17 | 8.01% |

MEP | kg N eq | 0.29 | 8.79% | 0.05 | 20.79% | 0.49 | 2.90% | 0.01 | 22.26% |

MD | kg Fe eq | 926.19 | 3.22% | 5.34 | 17.35% | 49.38 | 11.06% | 0.77 | 22.90% |

NLT | m^{2} | 0.24 | 8.28% | 0.05 | 18.78% | 0.43 | 4.67% | 0.01 | 24.03% |

ODP | kg CFC-11 eq | 0.00 | 8.59% | 0.00 | 17.82% | 0.00 | 4.61% | 0.00 | 17.63% |

PMFP | kg PM_{10} eq | 3.84 | 5.67% | 0.50 | 19.84% | 4.33 | 3.12% | 0.11 | 20.31% |

POFP | kg NMVOC | 5.76 | 9.12% | 1.51 | 21.63% | 14.03 | 2.77% | 0.26 | 26.69% |

TAP | kg SO_{2} eq | 5.30 | 8.90% | 1.00 | 19.21% | 8.40 | 3.12% | 0.25 | 16.97% |

TETP | kg 1,4-DB eq | 0.45 | 4.60% | 0.02 | 27.71% | 0.06 | 12.68% | 0.00 | 15.79% |

ULO | m^{2} × year | 23.29 | 9.86% | 3.50 | 29.32% | 6.75 | 21.00% | 0.17 | 8.93% |

WD | m^{3} | 8807.20 | 8.35% | 219.49 | 7.63% | 625.36 | 11.89% | 146.17 | 6.83% |

Damage Category | Reference Unit | Manufacturing | Construction | Use and Maintenance | EoL | ||||
---|---|---|---|---|---|---|---|---|---|

m | cv (%) | m | cv (%) | m | cv (%) | m | cv (%) | ||

Human health | DALY | 2.03 × 10^{−5} | 11.69% | 2.36 × 10^{−6} | 11.68% | 1.01 × 10^{−5} | 4.89% | −8.86 × 10^{−7} | 11.70% |

Resource | $ | 1.19 × 10^{2} | 4.01% | 8.78 × 10^{0} | 16.90% | 6.91 × 10^{1} | 5.60% | 1.88 × 10^{0} | 16.86% |

Ecosystem | Species per year | 4.58 × 10^{−3} | 13.53% | 5.18 × 10^{−4} | 9.75% | 2.75 × 10^{−3} | 6.26% | −1.33 × 10^{−4} | 7.64% |

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## Share and Cite

**MDPI and ACS Style**

Penadés-Plà, V.; García-Segura, T.; Martí, J.V.; Yepes, V.
An Optimization-LCA of a Prestressed Concrete Precast Bridge. *Sustainability* **2018**, *10*, 685.
https://doi.org/10.3390/su10030685

**AMA Style**

Penadés-Plà V, García-Segura T, Martí JV, Yepes V.
An Optimization-LCA of a Prestressed Concrete Precast Bridge. *Sustainability*. 2018; 10(3):685.
https://doi.org/10.3390/su10030685

**Chicago/Turabian Style**

Penadés-Plà, Vicent, Tatiana García-Segura, José V. Martí, and Víctor Yepes.
2018. "An Optimization-LCA of a Prestressed Concrete Precast Bridge" *Sustainability* 10, no. 3: 685.
https://doi.org/10.3390/su10030685